Dynamic Time Warping itself uses a distance measure as one of the steps in the algorithm . This paper aims at comparing the various distance measures used for Dynamic Time Warping . The result obtained by the Dynamic Time Warping is provided to the K-Nearest Neighbor Classifier to achieve Time Series Classification . The present invention relates to a distance measuring method and a stereo matching system using a video codec , the distance measuring method of the aspect of the invention , the first and the second comprising the steps of camera calibration by setting the reference motion vector , the first camera and encoding a second camera image with reference to the image , and the second when the predetermined block of the camera image is inter-prediction encoding and the step of comparing the motion vector and the reference motion vector of the specific block , a result of the comparison the motion vector is the reference motion vector and the same effective when the motion with the size value for display step of determining a parity value and the disparity value of the vector to first calculate the distance between the subject and displayed by the image of the predetermined block instead of characterized in that it comprises the steps : Stereo matching , disparity ( disparity ) This tool shall compute the applicable timeor distance-based separation minima and provide distance indicators supporting their correct delivery by the controller . Distance indicators have hence to account for aircraft separation compression effects but also for the uncertainties related to aircraft speed and wind evolution . This paper presents the principles used for the design of a demonstrator of such a separation delivery support platform called LORD . Among them , distance indicators and pressure readout can all be portable and easy to operate . As the name shows , distance indicators use distance as a signal-readout which means the result can be read by the naked eye . Piezoelectric biosensor also has an easy operation process and can achieve label-free , sensitive , and rapid detection . In this problem , metric learning approaches have played an important role by trying to bridge the semantic gap between image features and people 's identities . However , we believe that the traditional Mahalanobis distance is limited in capturing the diversity of visual phenomenon , and hence insufficient for complicated tasks such as people verification . In this paper , we introduce a novel discriminant function which generalizes the classical Mahalanobis distance . The said metric learning problem can be solved analytically for Mahalanobis distance metric due to simplistic nature of KRR in which we named KRRML . In addition , the WSILD further enhances the learned Mahalanobis distance metric by leveraging the within-class and between-class scatter matrix of doublets . We evaluate the proposed method with Labeled Faces in the Wild database , a large benchmark dataset targeted for unconstrained face verification . The experimental setup , data acquisition , and feature extraction are discussed briefly , and special attention is given to the statistical model used for a binary hypothesis test for damage diagnosis . This article focuses on the performance of different estimations of the Mahalanobis distance metric using robust estimates for location and scatter , and these alternative formulations are compared to traditional , less robust estimation methods . We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification . Our work is built upon the large margin nearest neighbor ( LMNN ) classification framework . It is sensitive to noise as well . To overcome the problem , Mahalanobis distance measure can be used . In the proposed work , classification of time series data is performed using time series shapelets and used Mahalanobis distance measure which is the measure of distribution between a point and distribution . Secondly , simulation data of both fault-free and faulty circuit is imported into Matlab using PSPICE and Matlab software data interface technology . Then , wavelet transform and Mahalanobis distance calculation of the simulation data is done in Matlab . Finally , the faulty component would be detected based on the Mahalanobis distances of both fault-free and faulty circuits . Before the data fusion or the establishment of soft sensor model , the consistency measure of original data is calculated and removes those data with low quality and precision . Based on the thought of Mahalanobis distance calculation , this paper , unlike other existing algorithms , proposes an algorithm , regarding the other sensors as a virtual population , to measure the credibility of each sensor from a vertical perspective , in which the correlation of sensors ' historical data , selected by sliding windows , is taken into consideration , so that an online monitoring way can be employed to attain more précised credibility sequence of sensors . The outcome of practical experimentation verifies the algorithm to be an effective one . In this paper , we propose a master-slave SoC structure composed of an ARM7-TDMI and a co-processor for Mahalanobis distance calculation . The SoC was implemented on an Actel ProASIC series M7A3P1000 FPGA . Abstract Anomaly detection by Mahalanobis-squared distance ( MSD ) is a popular unsupervised learning approach to structural health monitoring ( SHM ) . Despite the popularity and high applicability of the MSD-based anomaly detection method , some major challenging issues and limitations such as environmental variability , determination of an inappropriate threshold limit , estimation of an inaccurate covariance matrix , and non-Gaussianity of training data may lead to false alarms and erroneous results of damage detection . This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy . The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance , on data sets from monitoring systems installed on two bridges : the Z-24 Bridge and the Tamar Bridge . The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components . This reduction is interesting specially when the sensors are linked by a shared wireless network . This paper proposes new event-based sampling methods based on the Mahalanobis distance concept . Combined with an Event-Based State Estimator , they provide the same level of performance than other advanced sampling methods but keeping the computation simple enough to be executed by a smart sensor . It uses a corrosion model based on the decrease in corrosion rate over time to predict remaining life of an aged iron structures . The data-driven diagnostic tool developed uses Mahalanobis Distance analysis to detect anomalies in the behaviour of a structure . Bayesian Network models are then used as a fusion method , integrating remaining life predictions from the model-driven prognostic tool with information of possible anomalies from data-driven diagnostic tool to provide a probability distribution of predicted remaining life . In Section II we describe our basic approach . We then ( Section 111 ) present the first example , the synthesis of the Euclidean algorithm . Section IV provides a discussion of the method for introducing auxill iary functions , which is then illustrated by the construction of a two-function system for computing Cartesian products ( Section V ) . However , in the processing of data it is sufficient to implement the MS-PNS conversion only at the start and at the very last stages of processing , and in the intermediate transformations to use conversion from one to another modular system . This article proposes a process for the conversion of numbers from one system to another residual classes without using Euclidean algorithm , as its multiple implementation is the most time-consuming step of finding representations of numbers in the SRC and the inverse transform notions of SRC in the required number . The procedure relies on the use of tables prepared beforehand . Recently we proposed efficient optimal algorithms for GTSP based on quasi- and pseudo-pyramidal tours . As a consequence , we proved polynomial time solvability of the Euclidean GTSP in Grid Clusters defined by a grid of height at most 2 . In this short paper , we show how to extend this result to the case defined by grids of an arbitrary fixed height . We adopted data from MSNBC.COM website which is a free news data website with different categories of news and subjects . After generating the clusters by CURE algorithm , average of inter cluster and intra cluster are calculated and the results are compared with different similarity measures like Euclidean , Jaccard , projected Euclidean , cosine and fuzzy similarity . Finally behavior of clusters that made by CURE algorithm showed on a sequential data in a web usage domain with quantify our results by the way of explanations and list conclusions . The average of inter cluster and intra cluster are Calculated . the results are compared with different similarity measures like Euclidean , Jaccard , projected Euclidean , cosine and fuzzy similarity Finally showed behavior of clusters that made by OPTICS algorithm on a sequential data in a web usage domain . we performed a variety of experiments in the context of density based clustering , quantify our results by the way of explanation s and list conclusions . Initializing LBKeogh Dynamic Time Warping search using the Euclidean Distance Nearest Neighbor . Employing a fast Nearest Neighbor algorithm ( fastNN ) to increase computational efficiency . Although effective , LBKeogh begins the DTW search using the first candidate vector , which is typically arbitrarily chosen . In this work , we propose initializing the LBKeogh-based DTW search using the Euclidean Distance Nearest Neighbor , derived by a fast tree-based Nearest Neighbor technique . Our experimental results suggest that , on one hand , this simple NN-based approach is quite accurate for trajectory classification of digit and letter gesturing and can initialize the DTW search very efficiently , thus requiring about 20 % less search time than existing DTW implementations without any drop in recognition accuracy . Then , in the refining stage , key points are detected and described in the coarser scales using scale-invariant feature transform . The Euclidean distance strategy and the improved RANSAC based on prior energy function ( P-RANSAC ) are then employed to implement matching . On the basis of prior information , a model of energy function has been constructed to improve sampling strategy . Unfortunately , nodes generally have irregular distribution in reality , which tends to make deviations during the conversion between hop-counts and Euclidean distance , and as a result , the final estimated locations of nodes tend to deviate from the right location . Therefore , we analyzed the error during the hop-counts and Euclidean distance conversion , derived that the variance of error is related to hop-counts , and used the weighted least square method to correct the deviation . On this basis , we also limit the estimation location range of normal nodes according to the distances relationship between the estimated distances of normal nodes to anchor nodes and the distances of normal nodes estimated location to anchor nodes . We introduce stochastic proximity embedding ( SPE ) , a novel self‐organizing algorithm for producing meaningful underlying dimensions from proximity data . SPE attempts to generate low‐dimensional Euclidean embeddings that best preserve the similarities between a set of related observations . The method starts with an initial configuration , and iteratively refines it by repeatedly selecting pairs of objects at random , and adjusting their coordinates so that their distances on the map match more closely their respective proximities . Our evaluation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction . We find that Adamic-Adar and Euclidean distance measures are the best predictors . Our second contribution is proposing a factor graph model that incorporates a sentiment-based variant of cognitive balance theory . In this work , we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space . Existing ZSL works employ either euclidean or cosine distances . However , in a high-dimensional space where the projected class labels ( prototypes ) are sparse , these distances are suboptimal , resulting in a number of problems including hubness and domain shift . The Moore -- Penrose pseudoinverse has frequently been used as a surrogate but is not invariant under change of coordinates . Since the Euclidean Lie algebra carries a pencil of invariant bilinear forms that are indefinite , a family of alternative hyperbolic pseudoinverses is available . Generalized Gram matrices and the classification of screw systems are used to determine conditions for their existence . One line of work , exemplified by chopra2005learning and first applied to the caption - image relationship by socher2014grounded , requires the mapping to be distance - preserving : semantically similar objects are mapped to points that are nearby in the embedding space . A symmetric distance measure such as Euclidean or cosine distance is typically used . Since the visual - semantic hierarchy is an anti symmetric relation , we expect this approach to introduce systematic model error . subsection : Relationship to existing models Related prior few - shot work uses fixed pre - specified distance metrics such as Euclidean or cosine distance to perform classification . These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding . As a result , the right - hand side is exactly , where is defined in ( [ reference ] ) , giving us property ( A ) . It remains to show that property ( B ) also holds , specifically that the quantity ( [ reference ] ) can be approximated by the Euclidean inner - product with the patches and of shape ; we assume for that purpose that is a subsampled version of the patch shape by a factor . We remark that the kernel ( [ reference ] ) is the same as ( [ reference ] ) applied to layer by replacing by . In this paper we present a unified system for face verification ( is this the same person ) , recognition ( who is this person ) and clustering ( find common people among these faces ) . Our method is based on learning a Euclidean embedding per image using a deep convolutional network . The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances . The mini - batch size is 100 . The autoencoder is trained with a Euclidean cost function for reconstruction . On the MNIST dataset we use the sigmoid activation function in the last layer of the autoencoder and on the TFD dataset we use the linear activation function . Given our optimization procedure , we can rewrite the total loss for a pair of section : Algorithm 1 Training Using Euclidean Confusion where , γ ( y 1 , y 2 ) = 1 For the task of representing Atari frames , we found that an autoencoder could be used to successfully obtain an encoding function and achieve dimensionality reduction and feature extraction . Our autoencoder has 8 hidden layers , followed by a Euclidean loss layer , which computes the distance between the output features and the original input image . The hidden layers are reduced in dimension until maximal compression occurs with 128 units . In order to cope with the exponential time complexity of graph edit distance , several polynomial-time approximation algorithms have been proposed in recent years . The Hausdorff edit distance is a quadratic-time matching procedure for labeled graphs which reduces the edit distance to a correspondence problem between local substructures . For instance , to date one is not able to write an analytic algorithm to recognize , say , a face in a photo [ 3 ] . © Springer International Publishing Switzerland 2015 K. Riesen , Structural Pattern Recognition with Graph Edit Distance , Advances in Computer Vision and Pattern Recognition , DOI 10.1007/978 - 3 - 319 - 27252 - 8_1 3 4 Graphs have been widely used due to its expressive power to model complicated relationships . However , given a graph database Dg = { g1 , g2 , · · · , gn } , it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgraph and graph edit distance computation , which are NP-hard . In this paper , we study a novel DS-preserved mapping which maps graphs in a graph database Dg onto a multidimensional space Mg under a structural dimension M using a mapping function p ( ) . We propose a cost inference method that is based on a distribution estimation of edit operations . For this purpose , we employ an expectation maximization algorithm to learn mixture densities from a labeled sample of graphs and derive edit costs that are subsequently applied in the context of a graph edit distance computation framework . We evaluate the performance of the proposed distance model in comparison to another recently introduced learning model for edit costs . In this paper , we present POSGRAMI , a new hybrid approach for frequent subgraph mining based principally on approximate graph matching . To this end , POSGRAMI first uses an approximate structural similarity function based on graph edit distance function . POSGRAMI then uses a semantic vertices similarity function based on possibilistic information affinity function . It uses both similarity measures . An approximate structural similarity function based on graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function . Both structural and semantic filters contribute together to prune extracted frequent sets . We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust , cell-based simulation data . These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation , which provides a quantitative metric of similarity between graphs . The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration . We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism , and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB.ConclusionThe work presented here , including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository , has made feasible the automated development , training , and validation of computational models using morphology-based data . We describe these structured overlays as sets of possible overlay graphs for both stable and transient states due to network node leave and join events . We use the graph edit distance concept to determine the minimal communication cost of moving from a transient state to a stable state . Based on these communication costs we establish a theoretical lower bound on the maintenance overhead as the function of the churn rate . We demonstrate the benefits of these semantic representations in two tasks : entity ranking and computing document semantic similarity . To this end , we couple DBpedia 's structure with an information-theoretic measure of concept association , based on its explicit semantic relations , and compute semantic similarity using a Graph Edit Distance based measure , which finds the optimal matching between the documents ' entities using the Hungarian method . Experimental results show that our general model outperforms baselines built on top of traditional methods , and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks . To address this gap , this project compares theeffectiveness of the textual and structural representations of techniques to support the figure plagiarism detection . In addition to these two representations , the textual comparison method is designed to match the figure contents based on a word-gram representation using the Jaccard similarity measure , while the structural comparison method is designed to compare the text within the components as well as the relationship between the components of the figures using graph edit distance measure . These techniques are experimentally evaluated across the seven instances of figure plagiarism , in terms of their similarity values and the precision and recall metrics . We propose to improve the performance of i-vector based speaker verification by processing the i-vectors with a deep neural network before they are fed to a cosine distance or probabilistic linear discriminant analysis ( PLDA ) classifier . To this end we build on an existing model that we refer to as Non-linear Within Class Normalization ( NWCN ) and introduce a novel Speaker Classifier Network ( SCN ) . Audio recognition is based on the Gaussian Mixture modeling of the principal components of composite feature vectors , consisting of Mel-Frequency Cepstral Coefficients and Perceptual Linear Prediction coefficients of speech . Video recognition is based on combining three different classification algorithms : Principal Components Analysis with a modified Mahalanobis distance , sub-class Linear Discriminant Analysis ( featuring automatic sub-class generation ) with cosine distance and Bayesian classifier based on Gaussian modeling of intrapersonal differences . A nearest neighbor classification rule is applied . In order to find the required information quickly and efficiently in massive texts , this paper proposes a method of combining semantic fingerprint with cosine distance . After text preprocessing for Chinese texts , the Term Frequency-Inverse Document Frequency algorithm is used to extract feature words of the text , and then screen the text initially by the Simhash algorithm , finally compare these candidate texts tby using the cosine distance for the second similarity to extract the most similar texts . A corresponding cosine distance measure between NHFLTSs is proposed according to the relationship between the similarity measure and the distance measure , and we develop the technique for order preference by similarity to an ideal solution ( TOPSIS ) method to the obtained cosine distance measure . The main advantages of the proposed NHFLTS is defined on linguistic scale function , the decision makers can flexibly convert the linguistic information to semantic values , and the proposed cosine distance measure between NHFLTSs with TOPSIS method can deal with the related decision information not only from the point of view of algebra , but also from the point of view of geometry . Finally , the reasonableness and effectiveness of the proposed method is demonstrated by the illustrative example , which is also compared to the other existing methods . Considering that the cosine measure does not satisfy the axiom of similarity measure , then we propose a method to construct other similarity measures between q‐ROFSs based on the proposed cosine similarity and Euclidean distance measures , and it satisfies with the axiom of the similarity measure . Furthermore , we obtain a cosine distance measure between q‐ROFSs by using the relationship between the similarity and distance measures , then we extend technique for order of preference by similarity to the ideal solution method to the proposed cosine distance measure , which can deal with the related decision‐making problems not only from the point of view of geometry but also from the point of view of algebra . Finally , we give a practical example to illustrate the reasonableness and effectiveness of the proposed method , which is also compared with other existing methods . We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework . Our speaker clustering experiments on the GALE database clearly indicate that 1 ) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “ bag of acoustic features ” representation and statistical model-based distance metrics , 2 ) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric , 3 ) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines , and 4 ) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance . The deep architecture makes it possible to learn more abstract and internal features which are robust against the variations in viewpoint and lighting . Then we utilize the deep features of the training set to learn the specific distance metric of different datasets and combine it with Cosine distance metric together , multiple metric ensembles can measure the similarity between different images in a more comprehensive way . Finally , extensive experiments demonstrate that our method can improve the recognition performance effectively when compared with the state-of-the-art methods . As image data is typically noisy , but noise is substantially different from Gaussian , traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population . We show that replacing intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers , to some extend , a remedy to this problem . Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard ℓ2 intensity-based PCA . We introduce novel subspace-based methods for learning from the azimuth angle of surface normals for 3D face recognition . We show that the normal azimuth angles combined with Principal Component Analysis ( PCA ) using a cosine-based distance measure can be used for robust face recognition from facial surfaces . The proposed algorithms are well-suited for all types of 3D facial data including data produced by range cameras ( depth images ) , photometric stereo ( PS ) and shade-from-X ( SfX ) algorithms . Then the independent topological relations are extracted by training ICA assisted fuzzy SVMs , which can remove redundancy among the binary relations and reduce the dimension in complex spatial scene . In query processing the tftimesidf model is referenced , and the similarity is calculated by cosine distance function on the weight vectors of the query scene and each of spatial scenes in database . The experimental results show the recall factor and precision factor are improved compared with the query method without ICA and SVM . Then the independent topological relations are extracted by training ICA assisted fuzzy SVMs , which can remove redundancy among the binary relations and reduce the dimension in complex spatial scene . In query processing the tfxidf model is referenced , and the similarity is calculated by cosine distance function on the weight vectors of the query scene and each of spatial scenes in database . The experimental results show the recall factor and precision factor are improved compared with the query method without ICA and SVM . The Log-Gabor filter of 5 × 5 window size is used to extract the features . The Cosine based distance method is used for classification . Experimental results on the Yale B and Cafe database show that the proposed approach of normalized dataset improves the performance significantly for the face images with large illumination and expression variations . In contrast , population structure should be captured accurately for large k , but distance calculations are computationally intractable . Thus , we need an efficient way to compute genomic distance using large k. We hypothesized that the majority of k-mers are infrequent and would not contribute to the dot product in the cosine distance calculation . Thus , removing these k-mers from the calculation will possibly reduce computation time without impacting the cosine distance . Integrating different distance measures with modified k-Means algorithm help to select the proper distance measure for specific data mining application . This paper compare the results of modified k-Means with different distance measures like Euclidean Distance , Manhattan Distance , Minkowski Distance , Cosine Measure Distance and the Decimal Scaling normalization approach . Result Analysis is taken on various datasets from UCI machine dataset repository and shows that Mk-Means is advantageous and improve the effectiveness with normalized approach and Minkowski distance measure . During testing , we extract the 2 , 048 - dim CNN embedding in the last convolutional layer for an input image . The similarity between two images is calculated by a cosine distance for ranking . GAN training and testing . Correlation among weight vectors of the FC layer compromises the descriptor significantly when we consider the retrieval task under the Euclidean distance . In fact , a critical assumption of using Euclidean distance ( or equivalently the cosine distance after - normalization ) for retrieval is that the entries in the feature vector should be possibly independent . However , when the weight vectors are correlated , the FC descriptor – the projection on these weight vectors of the output of a previous CNN layer – will have correlated entries . The method is based on an improved genetic algorithm . Unlike the existing distance-based measures , the SMISM takes on the mean 3-D inter-space associated with each triangle in the overlap region to guide the range image registration . In addition , a hybrid genetic algorithm is able to register range images without need for pre-alignment , which is a key limitation always afflicting the well-known iterative closest point ( ICP ) method . Defining similarity measures is a crucial task when developing CBR applications . Particularly , when employing utility-based similarity measures rather than pure distance-based measures one is confronted with a difficult knowledge engineering task . Especially if different users of a CBR system have different demands on the case retrieval this task becomes really complex and sophisticated . Closeness centrality is a class of distance-based measures in the network analysis literature to quantify reachability of a given vertex ( or a group of vertices ) by other network agents . In this pa ...
Dynamic Time Warping itself uses a distance measure as one of the steps in the algorithm . This paper aims at comparing the various distance measures used for Dynamic Time Warping . The result obtained by the Dynamic Time Warping is provided to the K-Nearest Neighbor Classifier to achieve Time Series Classification .
The present invention relates to a distance measuring method and a stereo matching system using a video codec , the distance measuring method of the aspect of the invention , the first and the second comprising the steps of camera calibration by setting the reference motion vector , the first camera and encoding a second camera image with reference to the image , and the second when the predetermined block of the camera image is inter-prediction encoding and the step of comparing the motion vector and the reference motion vector of the specific block , a result of the comparison the motion vector is the reference motion vector and the same effective when the motion with the size value for display step of determining a parity value and the disparity value of the vector to first calculate the distance between the subject and displayed by the image of the predetermined block instead of characterized in that it comprises the steps : Stereo matching , disparity ( disparity )
This tool shall compute the applicable timeor distance-based separation minima and provide distance indicators supporting their correct delivery by the controller . Distance indicators have hence to account for aircraft separation compression effects but also for the uncertainties related to aircraft speed and wind evolution . This paper presents the principles used for the design of a demonstrator of such a separation delivery support platform called LORD .
Among them , distance indicators and pressure readout can all be portable and easy to operate . As the name shows , distance indicators use distance as a signal-readout which means the result can be read by the naked eye . Piezoelectric biosensor also has an easy operation process and can achieve label-free , sensitive , and rapid detection .
In this problem , metric learning approaches have played an important role by trying to bridge the semantic gap between image features and people 's identities . However , we believe that the traditional Mahalanobis distance is limited in capturing the diversity of visual phenomenon , and hence insufficient for complicated tasks such as people verification . In this paper , we introduce a novel discriminant function which generalizes the classical Mahalanobis distance .
The said metric learning problem can be solved analytically for Mahalanobis distance metric due to simplistic nature of KRR in which we named KRRML . In addition , the WSILD further enhances the learned Mahalanobis distance metric by leveraging the within-class and between-class scatter matrix of doublets . We evaluate the proposed method with Labeled Faces in the Wild database , a large benchmark dataset targeted for unconstrained face verification .
The experimental setup , data acquisition , and feature extraction are discussed briefly , and special attention is given to the statistical model used for a binary hypothesis test for damage diagnosis . This article focuses on the performance of different estimations of the Mahalanobis distance metric using robust estimates for location and scatter , and these alternative formulations are compared to traditional , less robust estimation methods .
We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification . Our work is built upon the large margin nearest neighbor ( LMNN ) classification framework .
It is sensitive to noise as well . To overcome the problem , Mahalanobis distance measure can be used . In the proposed work , classification of time series data is performed using time series shapelets and used Mahalanobis distance measure which is the measure of distribution between a point and distribution .
Secondly , simulation data of both fault-free and faulty circuit is imported into Matlab using PSPICE and Matlab software data interface technology . Then , wavelet transform and Mahalanobis distance calculation of the simulation data is done in Matlab . Finally , the faulty component would be detected based on the Mahalanobis distances of both fault-free and faulty circuits .
Before the data fusion or the establishment of soft sensor model , the consistency measure of original data is calculated and removes those data with low quality and precision . Based on the thought of Mahalanobis distance calculation , this paper , unlike other existing algorithms , proposes an algorithm , regarding the other sensors as a virtual population , to measure the credibility of each sensor from a vertical perspective , in which the correlation of sensors ' historical data , selected by sliding windows , is taken into consideration , so that an online monitoring way can be employed to attain more précised credibility sequence of sensors . The outcome of practical experimentation verifies the algorithm to be an effective one .
In this paper , we propose a master-slave SoC structure composed of an ARM7-TDMI and a co-processor for Mahalanobis distance calculation . The SoC was implemented on an Actel ProASIC series M7A3P1000 FPGA .
Abstract Anomaly detection by Mahalanobis-squared distance ( MSD ) is a popular unsupervised learning approach to structural health monitoring ( SHM ) . Despite the popularity and high applicability of the MSD-based anomaly detection method , some major challenging issues and limitations such as environmental variability , determination of an inappropriate threshold limit , estimation of an inaccurate covariance matrix , and non-Gaussianity of training data may lead to false alarms and erroneous results of damage detection .
This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy . The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance , on data sets from monitoring systems installed on two bridges : the Z-24 Bridge and the Tamar Bridge . The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components .
This reduction is interesting specially when the sensors are linked by a shared wireless network . This paper proposes new event-based sampling methods based on the Mahalanobis distance concept . Combined with an Event-Based State Estimator , they provide the same level of performance than other advanced sampling methods but keeping the computation simple enough to be executed by a smart sensor .
It uses a corrosion model based on the decrease in corrosion rate over time to predict remaining life of an aged iron structures . The data-driven diagnostic tool developed uses Mahalanobis Distance analysis to detect anomalies in the behaviour of a structure . Bayesian Network models are then used as a fusion method , integrating remaining life predictions from the model-driven prognostic tool with information of possible anomalies from data-driven diagnostic tool to provide a probability distribution of predicted remaining life .
In Section II we describe our basic approach . We then ( Section 111 ) present the first example , the synthesis of the Euclidean algorithm . Section IV provides a discussion of the method for introducing auxill iary functions , which is then illustrated by the construction of a two-function system for computing Cartesian products ( Section V ) .
However , in the processing of data it is sufficient to implement the MS-PNS conversion only at the start and at the very last stages of processing , and in the intermediate transformations to use conversion from one to another modular system . This article proposes a process for the conversion of numbers from one system to another residual classes without using Euclidean algorithm , as its multiple implementation is the most time-consuming step of finding representations of numbers in the SRC and the inverse transform notions of SRC in the required number . The procedure relies on the use of tables prepared beforehand .
Recently we proposed efficient optimal algorithms for GTSP based on quasi- and pseudo-pyramidal tours . As a consequence , we proved polynomial time solvability of the Euclidean GTSP in Grid Clusters defined by a grid of height at most 2 . In this short paper , we show how to extend this result to the case defined by grids of an arbitrary fixed height .
We adopted data from MSNBC.COM website which is a free news data website with different categories of news and subjects . After generating the clusters by CURE algorithm , average of inter cluster and intra cluster are calculated and the results are compared with different similarity measures like Euclidean , Jaccard , projected Euclidean , cosine and fuzzy similarity . Finally behavior of clusters that made by CURE algorithm showed on a sequential data in a web usage domain with quantify our results by the way of explanations and list conclusions .
The average of inter cluster and intra cluster are Calculated . the results are compared with different similarity measures like Euclidean , Jaccard , projected Euclidean , cosine and fuzzy similarity Finally showed behavior of clusters that made by OPTICS algorithm on a sequential data in a web usage domain . we performed a variety of experiments in the context of density based clustering , quantify our results by the way of explanation s and list conclusions .
Initializing LBKeogh Dynamic Time Warping search using the Euclidean Distance Nearest Neighbor . Employing a fast Nearest Neighbor algorithm ( fastNN ) to increase computational efficiency .
Although effective , LBKeogh begins the DTW search using the first candidate vector , which is typically arbitrarily chosen . In this work , we propose initializing the LBKeogh-based DTW search using the Euclidean Distance Nearest Neighbor , derived by a fast tree-based Nearest Neighbor technique . Our experimental results suggest that , on one hand , this simple NN-based approach is quite accurate for trajectory classification of digit and letter gesturing and can initialize the DTW search very efficiently , thus requiring about 20 % less search time than existing DTW implementations without any drop in recognition accuracy .
Then , in the refining stage , key points are detected and described in the coarser scales using scale-invariant feature transform . The Euclidean distance strategy and the improved RANSAC based on prior energy function ( P-RANSAC ) are then employed to implement matching . On the basis of prior information , a model of energy function has been constructed to improve sampling strategy .
Unfortunately , nodes generally have irregular distribution in reality , which tends to make deviations during the conversion between hop-counts and Euclidean distance , and as a result , the final estimated locations of nodes tend to deviate from the right location . Therefore , we analyzed the error during the hop-counts and Euclidean distance conversion , derived that the variance of error is related to hop-counts , and used the weighted least square method to correct the deviation . On this basis , we also limit the estimation location range of normal nodes according to the distances relationship between the estimated distances of normal nodes to anchor nodes and the distances of normal nodes estimated location to anchor nodes .
We introduce stochastic proximity embedding ( SPE ) , a novel self‐organizing algorithm for producing meaningful underlying dimensions from proximity data . SPE attempts to generate low‐dimensional Euclidean embeddings that best preserve the similarities between a set of related observations . The method starts with an initial configuration , and iteratively refines it by repeatedly selecting pairs of objects at random , and adjusting their coordinates so that their distances on the map match more closely their respective proximities .
Our evaluation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction . We find that Adamic-Adar and Euclidean distance measures are the best predictors . Our second contribution is proposing a factor graph model that incorporates a sentiment-based variant of cognitive balance theory .
In this work , we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space . Existing ZSL works employ either euclidean or cosine distances . However , in a high-dimensional space where the projected class labels ( prototypes ) are sparse , these distances are suboptimal , resulting in a number of problems including hubness and domain shift .
The Moore -- Penrose pseudoinverse has frequently been used as a surrogate but is not invariant under change of coordinates . Since the Euclidean Lie algebra carries a pencil of invariant bilinear forms that are indefinite , a family of alternative hyperbolic pseudoinverses is available . Generalized Gram matrices and the classification of screw systems are used to determine conditions for their existence .
One line of work , exemplified by chopra2005learning and first applied to the caption - image relationship by socher2014grounded , requires the mapping to be distance - preserving : semantically similar objects are mapped to points that are nearby in the embedding space . A symmetric distance measure such as Euclidean or cosine distance is typically used . Since the visual - semantic hierarchy is an anti symmetric relation , we expect this approach to introduce systematic model error .
subsection : Relationship to existing models Related prior few - shot work uses fixed pre - specified distance metrics such as Euclidean or cosine distance to perform classification . These studies can be seen as distance metric learning , but where all the learning occurs in the feature embedding , and a fixed metric is used given the learned embedding .
As a result , the right - hand side is exactly , where is defined in ( [ reference ] ) , giving us property ( A ) . It remains to show that property ( B ) also holds , specifically that the quantity ( [ reference ] ) can be approximated by the Euclidean inner - product with the patches and of shape ; we assume for that purpose that is a subsampled version of the patch shape by a factor . We remark that the kernel ( [ reference ] ) is the same as ( [ reference ] ) applied to layer by replacing by .
In this paper we present a unified system for face verification ( is this the same person ) , recognition ( who is this person ) and clustering ( find common people among these faces ) . Our method is based on learning a Euclidean embedding per image using a deep convolutional network . The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances .
The mini - batch size is 100 . The autoencoder is trained with a Euclidean cost function for reconstruction . On the MNIST dataset we use the sigmoid activation function in the last layer of the autoencoder and on the TFD dataset we use the linear activation function .
Given our optimization procedure , we can rewrite the total loss for a pair of section : Algorithm 1 Training Using Euclidean Confusion where , γ ( y 1 , y 2 ) = 1
For the task of representing Atari frames , we found that an autoencoder could be used to successfully obtain an encoding function and achieve dimensionality reduction and feature extraction . Our autoencoder has 8 hidden layers , followed by a Euclidean loss layer , which computes the distance between the output features and the original input image . The hidden layers are reduced in dimension until maximal compression occurs with 128 units .
In order to cope with the exponential time complexity of graph edit distance , several polynomial-time approximation algorithms have been proposed in recent years . The Hausdorff edit distance is a quadratic-time matching procedure for labeled graphs which reduces the edit distance to a correspondence problem between local substructures .
For instance , to date one is not able to write an analytic algorithm to recognize , say , a face in a photo [ 3 ] . © Springer International Publishing Switzerland 2015 K. Riesen , Structural Pattern Recognition with Graph Edit Distance , Advances in Computer Vision and Pattern Recognition , DOI 10.1007/978 - 3 - 319 - 27252 - 8_1 3 4
Graphs have been widely used due to its expressive power to model complicated relationships . However , given a graph database Dg = { g1 , g2 , · · · , gn } , it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgraph and graph edit distance computation , which are NP-hard . In this paper , we study a novel DS-preserved mapping which maps graphs in a graph database Dg onto a multidimensional space Mg under a structural dimension M using a mapping function p ( ) .
We propose a cost inference method that is based on a distribution estimation of edit operations . For this purpose , we employ an expectation maximization algorithm to learn mixture densities from a labeled sample of graphs and derive edit costs that are subsequently applied in the context of a graph edit distance computation framework . We evaluate the performance of the proposed distance model in comparison to another recently introduced learning model for edit costs .
In this paper , we present POSGRAMI , a new hybrid approach for frequent subgraph mining based principally on approximate graph matching . To this end , POSGRAMI first uses an approximate structural similarity function based on graph edit distance function . POSGRAMI then uses a semantic vertices similarity function based on possibilistic information affinity function .
It uses both similarity measures . An approximate structural similarity function based on graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function . Both structural and semantic filters contribute together to prune extracted frequent sets .
We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust , cell-based simulation data . These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation , which provides a quantitative metric of similarity between graphs . The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration . We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism , and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB.ConclusionThe work presented here , including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository , has made feasible the automated development , training , and validation of computational models using morphology-based data .
We describe these structured overlays as sets of possible overlay graphs for both stable and transient states due to network node leave and join events . We use the graph edit distance concept to determine the minimal communication cost of moving from a transient state to a stable state . Based on these communication costs we establish a theoretical lower bound on the maintenance overhead as the function of the churn rate .
We demonstrate the benefits of these semantic representations in two tasks : entity ranking and computing document semantic similarity . To this end , we couple DBpedia 's structure with an information-theoretic measure of concept association , based on its explicit semantic relations , and compute semantic similarity using a Graph Edit Distance based measure , which finds the optimal matching between the documents ' entities using the Hungarian method . Experimental results show that our general model outperforms baselines built on top of traditional methods , and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks .
To address this gap , this project compares theeffectiveness of the textual and structural representations of techniques to support the figure plagiarism detection . In addition to these two representations , the textual comparison method is designed to match the figure contents based on a word-gram representation using the Jaccard similarity measure , while the structural comparison method is designed to compare the text within the components as well as the relationship between the components of the figures using graph edit distance measure . These techniques are experimentally evaluated across the seven instances of figure plagiarism , in terms of their similarity values and the precision and recall metrics .
We propose to improve the performance of i-vector based speaker verification by processing the i-vectors with a deep neural network before they are fed to a cosine distance or probabilistic linear discriminant analysis ( PLDA ) classifier . To this end we build on an existing model that we refer to as Non-linear Within Class Normalization ( NWCN ) and introduce a novel Speaker Classifier Network ( SCN ) .
Audio recognition is based on the Gaussian Mixture modeling of the principal components of composite feature vectors , consisting of Mel-Frequency Cepstral Coefficients and Perceptual Linear Prediction coefficients of speech . Video recognition is based on combining three different classification algorithms : Principal Components Analysis with a modified Mahalanobis distance , sub-class Linear Discriminant Analysis ( featuring automatic sub-class generation ) with cosine distance and Bayesian classifier based on Gaussian modeling of intrapersonal differences . A nearest neighbor classification rule is applied .
In order to find the required information quickly and efficiently in massive texts , this paper proposes a method of combining semantic fingerprint with cosine distance . After text preprocessing for Chinese texts , the Term Frequency-Inverse Document Frequency algorithm is used to extract feature words of the text , and then screen the text initially by the Simhash algorithm , finally compare these candidate texts tby using the cosine distance for the second similarity to extract the most similar texts .
A corresponding cosine distance measure between NHFLTSs is proposed according to the relationship between the similarity measure and the distance measure , and we develop the technique for order preference by similarity to an ideal solution ( TOPSIS ) method to the obtained cosine distance measure . The main advantages of the proposed NHFLTS is defined on linguistic scale function , the decision makers can flexibly convert the linguistic information to semantic values , and the proposed cosine distance measure between NHFLTSs with TOPSIS method can deal with the related decision information not only from the point of view of algebra , but also from the point of view of geometry . Finally , the reasonableness and effectiveness of the proposed method is demonstrated by the illustrative example , which is also compared to the other existing methods .
Considering that the cosine measure does not satisfy the axiom of similarity measure , then we propose a method to construct other similarity measures between q‐ROFSs based on the proposed cosine similarity and Euclidean distance measures , and it satisfies with the axiom of the similarity measure . Furthermore , we obtain a cosine distance measure between q‐ROFSs by using the relationship between the similarity and distance measures , then we extend technique for order of preference by similarity to the ideal solution method to the proposed cosine distance measure , which can deal with the related decision‐making problems not only from the point of view of geometry but also from the point of view of algebra . Finally , we give a practical example to illustrate the reasonableness and effectiveness of the proposed method , which is also compared with other existing methods .
We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework . Our speaker clustering experiments on the GALE database clearly indicate that 1 ) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “ bag of acoustic features ” representation and statistical model-based distance metrics , 2 ) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric , 3 ) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines , and 4 ) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance .
The deep architecture makes it possible to learn more abstract and internal features which are robust against the variations in viewpoint and lighting . Then we utilize the deep features of the training set to learn the specific distance metric of different datasets and combine it with Cosine distance metric together , multiple metric ensembles can measure the similarity between different images in a more comprehensive way . Finally , extensive experiments demonstrate that our method can improve the recognition performance effectively when compared with the state-of-the-art methods .
As image data is typically noisy , but noise is substantially different from Gaussian , traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population . We show that replacing intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers , to some extend , a remedy to this problem . Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard ℓ2 intensity-based PCA .
We introduce novel subspace-based methods for learning from the azimuth angle of surface normals for 3D face recognition . We show that the normal azimuth angles combined with Principal Component Analysis ( PCA ) using a cosine-based distance measure can be used for robust face recognition from facial surfaces . The proposed algorithms are well-suited for all types of 3D facial data including data produced by range cameras ( depth images ) , photometric stereo ( PS ) and shade-from-X ( SfX ) algorithms .
Then the independent topological relations are extracted by training ICA assisted fuzzy SVMs , which can remove redundancy among the binary relations and reduce the dimension in complex spatial scene . In query processing the tftimesidf model is referenced , and the similarity is calculated by cosine distance function on the weight vectors of the query scene and each of spatial scenes in database . The experimental results show the recall factor and precision factor are improved compared with the query method without ICA and SVM .
Then the independent topological relations are extracted by training ICA assisted fuzzy SVMs , which can remove redundancy among the binary relations and reduce the dimension in complex spatial scene . In query processing the tfxidf model is referenced , and the similarity is calculated by cosine distance function on the weight vectors of the query scene and each of spatial scenes in database . The experimental results show the recall factor and precision factor are improved compared with the query method without ICA and SVM .
The Log-Gabor filter of 5 × 5 window size is used to extract the features . The Cosine based distance method is used for classification . Experimental results on the Yale B and Cafe database show that the proposed approach of normalized dataset improves the performance significantly for the face images with large illumination and expression variations .
In contrast , population structure should be captured accurately for large k , but distance calculations are computationally intractable . Thus , we need an efficient way to compute genomic distance using large k. We hypothesized that the majority of k-mers are infrequent and would not contribute to the dot product in the cosine distance calculation . Thus , removing these k-mers from the calculation will possibly reduce computation time without impacting the cosine distance .
Integrating different distance measures with modified k-Means algorithm help to select the proper distance measure for specific data mining application . This paper compare the results of modified k-Means with different distance measures like Euclidean Distance , Manhattan Distance , Minkowski Distance , Cosine Measure Distance and the Decimal Scaling normalization approach . Result Analysis is taken on various datasets from UCI machine dataset repository and shows that Mk-Means is advantageous and improve the effectiveness with normalized approach and Minkowski distance measure .
During testing , we extract the 2 , 048 - dim CNN embedding in the last convolutional layer for an input image . The similarity between two images is calculated by a cosine distance for ranking . GAN training and testing .
Correlation among weight vectors of the FC layer compromises the descriptor significantly when we consider the retrieval task under the Euclidean distance . In fact , a critical assumption of using Euclidean distance ( or equivalently the cosine distance after - normalization ) for retrieval is that the entries in the feature vector should be possibly independent . However , when the weight vectors are correlated , the FC descriptor – the projection on these weight vectors of the output of a previous CNN layer – will have correlated entries .
The method is based on an improved genetic algorithm . Unlike the existing distance-based measures , the SMISM takes on the mean 3-D inter-space associated with each triangle in the overlap region to guide the range image registration . In addition , a hybrid genetic algorithm is able to register range images without need for pre-alignment , which is a key limitation always afflicting the well-known iterative closest point ( ICP ) method .
Defining similarity measures is a crucial task when developing CBR applications . Particularly , when employing utility-based similarity measures rather than pure distance-based measures one is confronted with a difficult knowledge engineering task . Especially if different users of a CBR system have different demands on the case retrieval this task becomes really complex and sophisticated .
Closeness centrality is a class of distance-based measures in the network analysis literature to quantify reachability of a given vertex ( or a group of vertices ) by other network agents . In this pa ...
Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification Method for measuring distance and system for stereo matching Design Principles for a Separation Support Tool Allowing Optimized Runway Delivery Other Signal-Readout Technologies Beyond Mahalanobis distance: Learning second-order discriminant function for people verification Weighted Discriminant Analysis and Kernel Ridge Regression Metric Learning for Face Verification On using robust Mahalanobis distance estimations for feature discrimination in a damage detection scenario Efficiently Learning a Distance Metric for Large Margin Nearest Neighbor Classification Effect of Mahalanobis Distance on Time Series Classification Using Shapelets Method for Catastrophic Faults Location in Analog Circuits Based on Wavelet Transform and Mahalanobis Distance An online monitoring measure consistency computing algorithm by sliding window in multi-sensor system A master-slave SoC structure for HMM based speech recognition A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges Computation reduction of smart sensors for EBSE using Mahalanobis sampling Diagnostic and prognostic analysis tools for monitoring degradation in aged structures The Automatic Synthesis of Systems of Recursive Programs Преобразование представлений чисел в модулярной арифметике в системах остаточных классов с разными основаниями Pseudo-pyramidal Tours and Efficient Solvability of the Euclidean Generalized Traveling Salesman Problem in Grid Clusters CURE: CLUSTERING ON SEQUENTIAL DATA FOR WEB PERSONALIZATION: TESTS AND EXPERIMENTAL RESULTS OPTICS on Sequential Data: Experiments and Test Results Initialization of dynamic time warping using tree-based fast Nearest Neighbor Image Matching Based on Two-Column Histogram Hashing and Improved RANSAC Accurate and Fast DV-Hop Localization Algorithm in Irregular Networks Stochastic proximity embedding Exploiting sentiment homophily for link prediction Zero-Shot Learning on Semantic Class Prototype Graph Hyperbolic pseudoinverses for kinematics in the Euclidean group Order-Embeddings of Images and Language Learning to Compare: Relation Network for Few-Shot Learning Convolutional Kernel Networks FaceNet: A unified embedding for face recognition and clustering Adversarial Autoencoders Pairwise Confusion for Fine-Grained Visual Classification Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Improving Hausdorff Edit Distance Using Structural Node Context Structural Pattern Recognition with Graph Edit Distance Leveraging Graph Dimensions in Online Graph Search A probabilistic approach to learning costs for graph edit distance POSGRAMI: Possibilistic Frequent Subgraph Mining in a Single Large Graph Structural-semantic approach for approximate frequent subgraph mining Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search A graph theoretical lower bound on maintenance overhead of structured P2P overlays Knowledge-based graph document modeling TEXTUAL AND STRUCTURAL APPROACHES TO DETECTING FIGURE PLAGIARISM IN SCIENTIFIC PUBLICATIONS Modelling speaker and channel variability using deep neural networks for robust speaker verification The AIT Multimodal Person Identification System for CLEAR 2007 Research on Similarity Detection of Massive Text Based on Semantic Fingerprint Cosine Distance Measure between Neutrosophic Hesitant Fuzzy Linguistic Sets and Its Application in Multiple Criteria Decision Making Some cosine similarity measures and distance measures between q‐rung orthopair fuzzy sets Partially Supervised Speaker Clustering Deep feature representation and multiple metric ensembles for person re-identification in security surveillance system Principal component analysis of image gradient orientations for face recognition Robust Learning from Normals for 3D Face Recognition Sketch case based spatial topological data retrieval Sketch Case Based Spatial Topological Data Retrieval Performance analysis of DCT in logarithm domain and two -point normalization method for illumination and expression variation in face recognition Efficient Distance Calculations Between Genomes Using Mathematical Approximation Data Clustering: Integrating Different Distance Measures with Modified k-Means Algorithm Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro SVDNet for Pedestrian Retrieval Range Image Registration Using a Hybrid Genetic Algorithm and Surface Mean Inter-Space Measure State of Interest: Learning Personalized Utility Requirements Finding Critical Links for Closeness Centrality
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CVPR Workshops ICONIP (2) AAAI VLSI-DAT EBCCSP IJCAI LION ICAIS (3) RecSys ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ECCV ArXiv GbRPR Structural Pattern Recognition with Graph Edit Distance ICPR (3) IPMU (1) AICCSA WSDM SLT FG ECCV Workshops (2) PCM BCB SocProS (2) 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE International Conference on Computer Vision (ICCV) ECCBR Workshops
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