Recent works have demonstrated that the convolutional descriptor aggregation can provide state-of-the-art performance for image retrieval . In this paper , we propose a multi-center convolutional descriptor aggregation ( MCDA ) method to produce global image representation for image retrieval . We first present a feature map center selection method to eliminate the background information in the feature maps . Frequency features are described as the generalized Gaussian density ( GGD ) of Contourlet transform detail coefficients and LBP histogram of approximation coefficients . Further , we use closed-loop feedback to adjust weighting factor adoptively for image retrieval . Experiments show that average recall rate of this method is 12.08 % , 10.23 % higher than frequency domain method and LBP respectively . Information technology and Internet use and popularity of various digital terminal equipment , greatly enrich the database of digital images , at the same time , also brought new challenges to the image information retrieval . Content-based image retrieval technology is the key content in the field of image retrieval theory research , this article embarks from the content-based image retrieval technology system , studies the retrieval technology of the composition of the system framework and module , and expounds in detail the implementation process of content-based image retrieval technology . Among the above features , activity distribution ( in finding scars if any ) was found to have the least interobserver reproducibility . Therefore , in this study , we developed an image-based retrieval ( IBR ) and a computer-based diagnosis ( CAD ) system , focused on this feature in particular . The developed IBR and CAD algorithms start with automatic segmentation , boundary and landmark detection . With the developed system , user can lessen their efforts and laborious time on shading process by slightly tuning some attributes of those shaders even though CG artists have not a sufficient knowledge of shader attributes . INISIS supports several retrieval methods : based on the shader features as color , pattern , and texture , or image based retrieval . Image classification is well known technique of supervised learning . The improved method of image classification increases the working efficiency of image query retrieval . For the improvements of classification technique we used RBF neural network function for better prediction of feature used in image retrieval . To test the performance of the system , we used a long-term parking lot monitoring sequence . Fig. 2 shows the image retrieval query for experiment . In this situation , Line Passing and Duration Time are set . Their findings show that the adaptive kernel assignment can yield significantly higher annotation accuracy . After the initial results have been found from an image retrieval query , a fundamental problem is to re-rank the images toward providing the user withmore accurate and relevant results . The paper “ Image re-ranking system based on frequent closed patterns ” by Winn Voravuthikunchai , Bruno The convergence of the procedure is discussed . The algorithm can be applied in image search/retrieval tasks and is proposed in the MPEG-7 colour temperature descriptor for estimation of colour temperature of images . Global and local image feature extraction is one of the most common tasks in computer vision since they provide the basic information for further processes , and can be employed on several applications such as image search & retrieval , object recognition , 3D reconstruction , augmented reality , etc . The main parameters to evaluate a feature extraction algorithm are its discriminant capability , robustness and invariance behavior to certain transformations . Typically , it is achieved by utilizing a semantic embedding space to transfer knowledge from seen domain to unseen domain . Existing efforts mainly focus on single-modal retrieval task , especially image-based image retrieval ( IBIR ) . However , as a highlighted research topic in the field of hashing , cross-modal retrieval is more common in real-world applications . The image retrieval method includes : on receiving an image retrieval command , processing foreground features and background features of an original image to acquire a specified image ; according to the specified image and the original image , acquiring a target retrieval image ; according to the target retrieval image , performing image retrieval in a preset image library to acquire matching images matching with the original image . According to the technical scheme , by weighting the foreground features of the image and suppressing the background features , image retrieval speed is increased , using a whole image in retrieval is avoided , the negative influence of the background features of the image upon retrieval results is decreased , and image retrieval accuracy is improved . An Integrated Multimedia System with Learning Capabilities.- Video Indexing.- Global Motion Fourier Series Expansion for Video Indexing and Retrieval.- Feature Driven Visualization of Video Content for Interactive Indexing.- Conceptual Indexing of Television Images Based on Face and Caption Sizes and Locations.- Image Databases.- SIMPLIcity : Semantics-sensitive Integrated Matching for Picture LIbraries.- Semantic Indexing for Image Retrieval Using Description Logics.- An Iconic and Semantic Content Based Retrieval System for Histological Images.- Image Retrieval.- Image Retrieval by Colour and Texture Using Chromaticity Histograms and Wavelet Frames.- Adaptive Multi-Class Metric Content-Based Image Retrieval.- Integrating Visual and Textual Cues for Image Classification.- Benchmarking.- Evaluating the Performance of Content-Based Image Retrieval Systems.- Benchmarking for Content-Based Visual Information Search.- Posters.- Video Content Representation Based on Texture and Lighting.- Shape Similarity Measures , Properties and Constructions.- Leaf Image Retrieval with Shape Features.- A Software Framework for Combining Iconic and Semantic Content for Retrieval of Histological Images.- A Ground-Truth Training Set for Hierarchical Clustering in Content-based Image Retrieval.- Query Models and Languages for Geographical Information Systems.- Content-Based Image Retrieval By Relevance Feedback.- The human process is a natural categorization , which underlies many natural abilities such as image recovery , language comprehension , decision making or pattern recognition . In this paper , the focus is on the use of similarities in image retrieval search using near sets of similarity approaches . The results showed that a general framework for Near set is compatible with these foundations , and that similarity measurements can be involved in all steps of the image research process . He was with University of Texas at San Antonio ( UTSA ) and Johns Hopkins University ( JHU ) as a research scholar . His research interests include image retrieval and classification , 3D shape recognition , person re - identification , semantic segmentation and deep learning . More information can be found in his homepage : . He received a PhD degree from the University of Cambridge in 2002 , where he worked on 3D reconstruction of architecture from images . His research interests include image - based modeling , automated video surveillance , and image search . Anton van den Hengel is a Professor at the University of Adelaide and the founding Director of The Australian Centre for Visual Technologies ( ACVT ) . M is a follow up version of the dataset introduced by . The JFT - 300 M dataset is closely related and derived from the data which powers the Image Search . In this version , the dataset has 300 M images and 375 M labels , on average each image has 1.26 labels . ( 25.6 M parameters ) , as this architecture is adopted in the literature in many works both on image classification and instance retrieval . paragraph : Image search : from local features to CNN . “ This method is evaluated with person re - ID , which aims to spot the target person in different cameras . This has been recently viewed as an image retrieval problem . This paper addresses three challenges . As an interesting yet different method which can also filter low - quality images , [ reference ] learns to aggregate the raw face images and then computes a descriptor . Our aggregation approach is inspired by the image retrieval literature on aggregating local descriptors [ reference ][ reference ] . Namely , Jégou and Zisserman [ reference ] find that , compared to simple average - pooling , Fisher Vector encoding and T - embedding increase the contrast between the similarity scores of matching and mismatching local descriptors . Motivated by this fact , we make use of a trainable aggregation layer , NetVLAD [ reference ] , and improve it for the face recognition task . Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality . The main challenge is to map different modalities into a common semantic space , in which distance between concepts in different modalities can be well modeled . DCMH is an end-to-end learning framework with deep neural networks , one for each modality , to perform feature learning from scratch . Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications . As a result , an efficient linear semantic down mapping is jointly learned for multimodal data , leading to a common space where they can be compared . The mechanism of " feature up-lifting and down-projecting " works seamlessly as a whole , which accomplishes crossmodal retrieval tasks very well . The proposed method , named as shared discriminative semantic representation learning ( \textbf{SDSRL } ) , is tested on two public multimodal dataset for both within- and inter- modal retrieval . Besides , both inter-modality and intra-modality reward signals can be extracted to provide complementary clues for boosting cross-modal correlation learning . Experiments are conducted to verify the performance of our proposed approach on crossmodal retrieval , compared with 11 state-of-the-art methods on 3 datasets . In this paper , we consider a cross-modal retrieval scenario of Western classical music . Given a short monophonic musical theme in symbolic notation as query , the objective is to find relevant audio recordings in a database . These monophonic themes ( usually four bars long ) are often the most memorable parts of a piece of music . In this paper , we report on a systematic study considering a cross-modal retrieval scenario . Using a musical theme as a query , the objective is to identify all related music recordings from a given audio collection . Moreover , a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning , inter-modality similarity preserving learning , semantic label preserving learning and hash functions learning with bit balanced constraint simultaneously . We conduct extensive experiments for both unimodal and cross-modal retrieval tasks on three widely-used multimodal retrieval datasets . The experimental result demonstrates that DSMHN significantly outperforms state-of-the-art methods . An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors , which can be trained from both paired and partially paired data in linear time . A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval . Abstract Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality . The main challenge is to learn the semantic correlations between different modalities and measure the distance across modalities . Our approach uses Latent Semantic Indexing , a method that uses co-occurrence statistics to uncover hidden semantics . This paper shows how this method , that has proven successful in both monolingual and cross lingual text retrieval , can be used for multi-modal and cross-modal information retrieval . Experiments with an on-line newspaper archive show that Latent Semantic Indexing can outperform both content based and context based approaches and that it is a promising approach for indexing visual and multi-modal data . Due to this flexibility , our SEH framework could handle both single-modal retrieval and cross-modal retrieval simultaneously . Experimental evidence shows this method achieves much better performance in both single- and cross-modal retrieval tasks as compared to state-of-the-art approaches . To achieve better results , a novel weighted multi-modal feature fusion method is designed to obtain the final feature representation . After many experimental verification , we found that different feature fusion weights for training and testing make the method robust to multi-modal person identification . Experiments on the iQIYI-VID-2019 dataset show that our multi-modal feature fusion strategy effectively improves the accuracy of person identification . A discloses a technique related to multi-modal identification using classifier combination techniques , multimodal identification system comprising in one embodiment , a set of classifiers to classify the feature stream obtained from the audio / video source I will provide a. SOLUTION In this system , when combining the output from the discriminator there is variation in reliability by using the classifier combination techniques , the robust way , in the conventional " each identifier " without ( or , which and in combination ) incorporating the confidence to " each class " carry out an objective evaluation ( weighting ) . The system may individually operate the feature identifier is dispersed to a plurality of machines of subscription type . An Empirical Study.- Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks.- Between Two Extremes : Examining Decompositions of the Ensemble Objective Function.- Data Partitioning Evaluation Measures for Classifier Ensembles.- Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation.- Ensemble Confidence Estimates Posterior Probability.- Applications.- Using Domain Knowledge in the Random Subspace Method : Application to the Classification of Biomedical Spectra.- An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble.- Speaker Verification Using Adapted User-Dependent Multilevel Fusion.- Multi-modal Person Recognition for Vehicular Applications.- Using an Ensemble of Classifiers to Audit a Production Classifier.- Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance.- Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation.- Designing Multiple Classifier Systems for Face Recognition.- Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data . Et leurs apports à la reconnaissance de personnes . This article presents the different steps used to recognize characters for multi-modal person recognition systems in video ( ANR REPRE challenge ) . Text detection is achieved by a technique based on the text features ( texture , color , contrast , geometry , temporal information , measure of accumulated gradients ) . In this study both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of a test structure ( a 3- story steel frame subjected to a series of earthquake and white noise excitations back to back ) using both input and output response data or output only measurement and identify the damage location . For the modal-based identification , the multi-variant autoregressive model ( MV-AR model ) is used to identify the dynamic characteristics of structure . The MV-AR model parameters are then used to develop the vectors of autoregressive model and Mahalanobis distance , and then to identify the damage features and locate the damage . While the approaches based on RGB modality have been widely studied to re-identify people , other modalities could be exploited as additional information sources , like depth and skeleton modalities . In this paper , we perform multi- and single-modal person re-identification using RGB , depth and skeleton modalities obtained by RGB-D sensors . First of all , the depth and RGB images are divided into three regions of head , torso and legs . Multi-modal Person Identification ( MMPI ) is a software technology for multi-modal recognition of persons according to their faces and the way they walk . The recognition process is based on analysis of detected face images and motion capture data — both acquired by motion capturing devices , including popular Microsoft Kinect and ASUS Xtion . We show that , using multiple CNNs with multimodal fusion at the feature-level , we significantly outperform systems that use unimodal representation . We study weighted feature , bilinear , and compact bilinear feature-level fusion algorithms for multimodal biometric person identification . Finally , We propose generalized compact bilinear fusion algorithm to deploy both the weighted feature fusion and compact bilinear schemes . Studies also demonstrate that fusion of multiple face modalities improve performance as compared with singlemodal face recognition . This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables . Advantages and disadvantages of each modality for face recognition are analyzed . In the Query-by-sketch image retrieval , feature extraction method is important , because the retrieval result depend on image feature . In this paper , we propose the query-by-sketch image retrieval using Edge Relation Histogram ( ERH ) as global and local feature . Nevertheless a large class of users ' queries request images having regions , or objects , in well-determined spatial arrangements . Within the framework of query-by-sketch image retrieval , we propose a structured spatial layout representation . The approach provides a way to extract the image content starting from basic features and combining them in a higher-level description of spatial layout of components , characterizing the semantics of the scene . This degrades retrieval performance , since successful retrieval depends on the quality of the sketch query image drawn by the user . To address this issue , we propose a real-time stroke guidance for freehand sketch retrieval that continuously displays next-stroke shadow sketches on the canvas based on the user 's step-by-step partial strokes . We train a stroke guidance network that learns the mapping between the step-wise stroke relations to predict the user 's next stroke . We address the problem of large scale sketch based image retrieval , searching in a database of over a million images . The search is based on a descriptor that elegantly addresses the asymmetry between the binary user sketch on the one hand and the full color image on the other hand . This demo will interactively show a system that exploits a novel user interface , running on Tablet PCs or graphic tablets , that provides query-by-sketch based image retrieval using color sketches . The system uses Angular Radial Partitioning ( ARP ) for the edge information in the sketches and color moments in the CIELAB space , combined with a distance metric that is robust to deviations in color as they usually need to be taken into account with user-generated color sketches . Further , we incorporate semantic keywords into our GF-HOG system to enable the use of annotated sketches for image search . A novel graph-based measure of semantic similarity is proposed and two applications explored : semantic sketch based image retrieval and a semantic photo montage .
Recent works have demonstrated that the convolutional descriptor aggregation can provide state-of-the-art performance for image retrieval . In this paper , we propose a multi-center convolutional descriptor aggregation ( MCDA ) method to produce global image representation for image retrieval . We first present a feature map center selection method to eliminate the background information in the feature maps .
Frequency features are described as the generalized Gaussian density ( GGD ) of Contourlet transform detail coefficients and LBP histogram of approximation coefficients . Further , we use closed-loop feedback to adjust weighting factor adoptively for image retrieval . Experiments show that average recall rate of this method is 12.08 % , 10.23 % higher than frequency domain method and LBP respectively .
Information technology and Internet use and popularity of various digital terminal equipment , greatly enrich the database of digital images , at the same time , also brought new challenges to the image information retrieval . Content-based image retrieval technology is the key content in the field of image retrieval theory research , this article embarks from the content-based image retrieval technology system , studies the retrieval technology of the composition of the system framework and module , and expounds in detail the implementation process of content-based image retrieval technology .
Among the above features , activity distribution ( in finding scars if any ) was found to have the least interobserver reproducibility . Therefore , in this study , we developed an image-based retrieval ( IBR ) and a computer-based diagnosis ( CAD ) system , focused on this feature in particular . The developed IBR and CAD algorithms start with automatic segmentation , boundary and landmark detection .
With the developed system , user can lessen their efforts and laborious time on shading process by slightly tuning some attributes of those shaders even though CG artists have not a sufficient knowledge of shader attributes . INISIS supports several retrieval methods : based on the shader features as color , pattern , and texture , or image based retrieval .
Image classification is well known technique of supervised learning . The improved method of image classification increases the working efficiency of image query retrieval . For the improvements of classification technique we used RBF neural network function for better prediction of feature used in image retrieval .
To test the performance of the system , we used a long-term parking lot monitoring sequence . Fig. 2 shows the image retrieval query for experiment . In this situation , Line Passing and Duration Time are set .
Their findings show that the adaptive kernel assignment can yield significantly higher annotation accuracy . After the initial results have been found from an image retrieval query , a fundamental problem is to re-rank the images toward providing the user withmore accurate and relevant results . The paper “ Image re-ranking system based on frequent closed patterns ” by Winn Voravuthikunchai , Bruno
The convergence of the procedure is discussed . The algorithm can be applied in image search/retrieval tasks and is proposed in the MPEG-7 colour temperature descriptor for estimation of colour temperature of images .
Global and local image feature extraction is one of the most common tasks in computer vision since they provide the basic information for further processes , and can be employed on several applications such as image search & retrieval , object recognition , 3D reconstruction , augmented reality , etc . The main parameters to evaluate a feature extraction algorithm are its discriminant capability , robustness and invariance behavior to certain transformations .
Typically , it is achieved by utilizing a semantic embedding space to transfer knowledge from seen domain to unseen domain . Existing efforts mainly focus on single-modal retrieval task , especially image-based image retrieval ( IBIR ) . However , as a highlighted research topic in the field of hashing , cross-modal retrieval is more common in real-world applications .
The image retrieval method includes : on receiving an image retrieval command , processing foreground features and background features of an original image to acquire a specified image ; according to the specified image and the original image , acquiring a target retrieval image ; according to the target retrieval image , performing image retrieval in a preset image library to acquire matching images matching with the original image . According to the technical scheme , by weighting the foreground features of the image and suppressing the background features , image retrieval speed is increased , using a whole image in retrieval is avoided , the negative influence of the background features of the image upon retrieval results is decreased , and image retrieval accuracy is improved .
An Integrated Multimedia System with Learning Capabilities.- Video Indexing.- Global Motion Fourier Series Expansion for Video Indexing and Retrieval.- Feature Driven Visualization of Video Content for Interactive Indexing.- Conceptual Indexing of Television Images Based on Face and Caption Sizes and Locations.- Image Databases.- SIMPLIcity : Semantics-sensitive Integrated Matching for Picture LIbraries.- Semantic Indexing for Image Retrieval Using Description Logics.- An Iconic and Semantic Content Based Retrieval System for Histological Images.- Image Retrieval.- Image Retrieval by Colour and Texture Using Chromaticity Histograms and Wavelet Frames.- Adaptive Multi-Class Metric Content-Based Image Retrieval.- Integrating Visual and Textual Cues for Image Classification.- Benchmarking.- Evaluating the Performance of Content-Based Image Retrieval Systems.- Benchmarking for Content-Based Visual Information Search.- Posters.- Video Content Representation Based on Texture and Lighting.- Shape Similarity Measures , Properties and Constructions.- Leaf Image Retrieval with Shape Features.- A Software Framework for Combining Iconic and Semantic Content for Retrieval of Histological Images.- A Ground-Truth Training Set for Hierarchical Clustering in Content-based Image Retrieval.- Query Models and Languages for Geographical Information Systems.- Content-Based Image Retrieval By Relevance Feedback.-
The human process is a natural categorization , which underlies many natural abilities such as image recovery , language comprehension , decision making or pattern recognition . In this paper , the focus is on the use of similarities in image retrieval search using near sets of similarity approaches . The results showed that a general framework for Near set is compatible with these foundations , and that similarity measurements can be involved in all steps of the image research process .
He was with University of Texas at San Antonio ( UTSA ) and Johns Hopkins University ( JHU ) as a research scholar . His research interests include image retrieval and classification , 3D shape recognition , person re - identification , semantic segmentation and deep learning . More information can be found in his homepage : .
He received a PhD degree from the University of Cambridge in 2002 , where he worked on 3D reconstruction of architecture from images . His research interests include image - based modeling , automated video surveillance , and image search . Anton van den Hengel is a Professor at the University of Adelaide and the founding Director of The Australian Centre for Visual Technologies ( ACVT ) .
M is a follow up version of the dataset introduced by . The JFT - 300 M dataset is closely related and derived from the data which powers the Image Search . In this version , the dataset has 300 M images and 375 M labels , on average each image has 1.26 labels .
( 25.6 M parameters ) , as this architecture is adopted in the literature in many works both on image classification and instance retrieval . paragraph : Image search : from local features to CNN . “
This method is evaluated with person re - ID , which aims to spot the target person in different cameras . This has been recently viewed as an image retrieval problem . This paper addresses three challenges .
As an interesting yet different method which can also filter low - quality images , [ reference ] learns to aggregate the raw face images and then computes a descriptor . Our aggregation approach is inspired by the image retrieval literature on aggregating local descriptors [ reference ][ reference ] . Namely , Jégou and Zisserman [ reference ] find that , compared to simple average - pooling , Fisher Vector encoding and T - embedding increase the contrast between the similarity scores of matching and mismatching local descriptors . Motivated by this fact , we make use of a trainable aggregation layer , NetVLAD [ reference ] , and improve it for the face recognition task .
Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality . The main challenge is to map different modalities into a common semantic space , in which distance between concepts in different modalities can be well modeled .
DCMH is an end-to-end learning framework with deep neural networks , one for each modality , to perform feature learning from scratch . Experiments on three real datasets with image-text modalities show that DCMH can outperform other baselines to achieve the state-of-the-art performance in cross-modal retrieval applications .
As a result , an efficient linear semantic down mapping is jointly learned for multimodal data , leading to a common space where they can be compared . The mechanism of " feature up-lifting and down-projecting " works seamlessly as a whole , which accomplishes crossmodal retrieval tasks very well . The proposed method , named as shared discriminative semantic representation learning ( \textbf{SDSRL } ) , is tested on two public multimodal dataset for both within- and inter- modal retrieval .
Besides , both inter-modality and intra-modality reward signals can be extracted to provide complementary clues for boosting cross-modal correlation learning . Experiments are conducted to verify the performance of our proposed approach on crossmodal retrieval , compared with 11 state-of-the-art methods on 3 datasets .
In this paper , we consider a cross-modal retrieval scenario of Western classical music . Given a short monophonic musical theme in symbolic notation as query , the objective is to find relevant audio recordings in a database .
These monophonic themes ( usually four bars long ) are often the most memorable parts of a piece of music . In this paper , we report on a systematic study considering a cross-modal retrieval scenario . Using a musical theme as a query , the objective is to identify all related music recordings from a given audio collection .
Moreover , a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning , inter-modality similarity preserving learning , semantic label preserving learning and hash functions learning with bit balanced constraint simultaneously . We conduct extensive experiments for both unimodal and cross-modal retrieval tasks on three widely-used multimodal retrieval datasets . The experimental result demonstrates that DSMHN significantly outperforms state-of-the-art methods .
An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors , which can be trained from both paired and partially paired data in linear time . A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval .
Abstract Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality . The main challenge is to learn the semantic correlations between different modalities and measure the distance across modalities .
Our approach uses Latent Semantic Indexing , a method that uses co-occurrence statistics to uncover hidden semantics . This paper shows how this method , that has proven successful in both monolingual and cross lingual text retrieval , can be used for multi-modal and cross-modal information retrieval . Experiments with an on-line newspaper archive show that Latent Semantic Indexing can outperform both content based and context based approaches and that it is a promising approach for indexing visual and multi-modal data .
Due to this flexibility , our SEH framework could handle both single-modal retrieval and cross-modal retrieval simultaneously . Experimental evidence shows this method achieves much better performance in both single- and cross-modal retrieval tasks as compared to state-of-the-art approaches .
To achieve better results , a novel weighted multi-modal feature fusion method is designed to obtain the final feature representation . After many experimental verification , we found that different feature fusion weights for training and testing make the method robust to multi-modal person identification . Experiments on the iQIYI-VID-2019 dataset show that our multi-modal feature fusion strategy effectively improves the accuracy of person identification .
A discloses a technique related to multi-modal identification using classifier combination techniques , multimodal identification system comprising in one embodiment , a set of classifiers to classify the feature stream obtained from the audio / video source I will provide a. SOLUTION In this system , when combining the output from the discriminator there is variation in reliability by using the classifier combination techniques , the robust way , in the conventional " each identifier " without ( or , which and in combination ) incorporating the confidence to " each class " carry out an objective evaluation ( weighting ) . The system may individually operate the feature identifier is dispersed to a plurality of machines of subscription type .
An Empirical Study.- Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks.- Between Two Extremes : Examining Decompositions of the Ensemble Objective Function.- Data Partitioning Evaluation Measures for Classifier Ensembles.- Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation.- Ensemble Confidence Estimates Posterior Probability.- Applications.- Using Domain Knowledge in the Random Subspace Method : Application to the Classification of Biomedical Spectra.- An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble.- Speaker Verification Using Adapted User-Dependent Multilevel Fusion.- Multi-modal Person Recognition for Vehicular Applications.- Using an Ensemble of Classifiers to Audit a Production Classifier.- Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance.- Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation.- Designing Multiple Classifier Systems for Face Recognition.- Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data .
Et leurs apports à la reconnaissance de personnes . This article presents the different steps used to recognize characters for multi-modal person recognition systems in video ( ANR REPRE challenge ) . Text detection is achieved by a technique based on the text features ( texture , color , contrast , geometry , temporal information , measure of accumulated gradients ) .
In this study both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of a test structure ( a 3- story steel frame subjected to a series of earthquake and white noise excitations back to back ) using both input and output response data or output only measurement and identify the damage location . For the modal-based identification , the multi-variant autoregressive model ( MV-AR model ) is used to identify the dynamic characteristics of structure . The MV-AR model parameters are then used to develop the vectors of autoregressive model and Mahalanobis distance , and then to identify the damage features and locate the damage .
While the approaches based on RGB modality have been widely studied to re-identify people , other modalities could be exploited as additional information sources , like depth and skeleton modalities . In this paper , we perform multi- and single-modal person re-identification using RGB , depth and skeleton modalities obtained by RGB-D sensors . First of all , the depth and RGB images are divided into three regions of head , torso and legs .
Multi-modal Person Identification ( MMPI ) is a software technology for multi-modal recognition of persons according to their faces and the way they walk . The recognition process is based on analysis of detected face images and motion capture data — both acquired by motion capturing devices , including popular Microsoft Kinect and ASUS Xtion .
We show that , using multiple CNNs with multimodal fusion at the feature-level , we significantly outperform systems that use unimodal representation . We study weighted feature , bilinear , and compact bilinear feature-level fusion algorithms for multimodal biometric person identification . Finally , We propose generalized compact bilinear fusion algorithm to deploy both the weighted feature fusion and compact bilinear schemes .
Studies also demonstrate that fusion of multiple face modalities improve performance as compared with singlemodal face recognition . This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables . Advantages and disadvantages of each modality for face recognition are analyzed .
In the Query-by-sketch image retrieval , feature extraction method is important , because the retrieval result depend on image feature . In this paper , we propose the query-by-sketch image retrieval using Edge Relation Histogram ( ERH ) as global and local feature .
Nevertheless a large class of users ' queries request images having regions , or objects , in well-determined spatial arrangements . Within the framework of query-by-sketch image retrieval , we propose a structured spatial layout representation . The approach provides a way to extract the image content starting from basic features and combining them in a higher-level description of spatial layout of components , characterizing the semantics of the scene .
This degrades retrieval performance , since successful retrieval depends on the quality of the sketch query image drawn by the user . To address this issue , we propose a real-time stroke guidance for freehand sketch retrieval that continuously displays next-stroke shadow sketches on the canvas based on the user 's step-by-step partial strokes . We train a stroke guidance network that learns the mapping between the step-wise stroke relations to predict the user 's next stroke .
We address the problem of large scale sketch based image retrieval , searching in a database of over a million images . The search is based on a descriptor that elegantly addresses the asymmetry between the binary user sketch on the one hand and the full color image on the other hand .
This demo will interactively show a system that exploits a novel user interface , running on Tablet PCs or graphic tablets , that provides query-by-sketch based image retrieval using color sketches . The system uses Angular Radial Partitioning ( ARP ) for the edge information in the sketches and color moments in the CIELAB space , combined with a distance metric that is robust to deviations in color as they usually need to be taken into account with user-generated color sketches .
Further , we incorporate semantic keywords into our GF-HOG system to enable the use of annotated sketches for image search . A novel graph-based measure of semantic similarity is proposed and two applications explored : semantic sketch based image retrieval and a semantic photo montage .
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Medical Imaging: Computer-Aided Diagnosis SIGGRAPH ASIA Posters VISAPP (1) FUZZ-IEEE IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv 2017 IEEE International Conference on Computer Vision (ICCV) ACCV CVPR IJCAI ICASSP ICASSP SIGIR RIAO ACM Multimedia CORIA SBIM ECIR
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