Within the statistical decision theory framework , This paper focuses on specific objectives in face recognition , and proposes the BP neural network classification methods which is under the Linex loss function , and proves that the convergence of BP neural network under the Linex loss function . The image recognition experiments with the ORL face database in Cambridge show that this method can effectively solve the traditional problems in specific target face recognition of BP neural network . Two different implementations are described ; one using an exact line search to find the minimum of the error along the current search direction , the other avoids the line search by controlling the positive indefiniteness of the Hessian matrix . The two implementations are compared and evaluated in the context of an image recognition problem using input bit-maps with a resolution of 128 by 128 pixels . In this method , the neural network responds in real-time . This is connected with reorganizing classical paradigm of image recognition process . The character of recognition stage is performed before the image segmentation . 2009 Thesis directed by : Professor Rama Chellappa Department of Electrical and Computer Engineering Recently , face recognition based on video has gained wide interest especially due to its role in surveillance systems . Video-based recognition has superior advantages over image-based recognition because a video contains image sequences as well as temporal information . However , surveillance videos are generally of lowresolution and contain faces mostly in non-frontal poses . The invention provides a ground object information collection device and a program thereof , a self-driving position recognition device and a navigation device , which can quickly collect the ground object information after construction when the ground object position shifts or the ground object category alters owing to construction . The devices are provided with a ground object image recognition unit carrying out image recognition processing to the ground objects included in the image information of the vehicle surroundings , a construction information acquisition unit for acquiring the construction information in the construction zone , a construction storage unit for storing the acquired construction information , a construction completion judging unit for judging whether the construction indicated by the construction information completes when the vehicle drives on the roads corresponding to the constriction zone related to the stored construction information , and a ground object study unit for enabling the ground object recognition unit to carry out image recognition processing to the ground object in the construction zone related to the construction information after the construction is judged to complete and generating study ground object information including the position information and attribution information of the recognized ground object based on the image recognition result and the vehicle position information . It is of great importance to investigate the domain adaptation problem of image object recognition , because now image data is available from a variety of source domains . To understand the changes in data distributions across domains , we study both the input and output kernel spaces for cross-domain learning situations , where most labeled training images are from a source domain and testing images are from a different target domain . He has over 30 published papers and/or technical presentations while spearheading over 40 international scientific and engineering conferences/workshops as a steering committee member while assigned in Europe . Professor Santiago has experience in many engineering disciplines and missions including : control and modeling of large flexible space structures , communications system , electro-optics , high-energy lasers , missile seekers/sensors for precision guided munitions , image processing/recognition , information technologies , space , air and missile warning , missile defense , and homeland defense . The system comprises a terminal device . By means of the terminal device , target objects are captured to establish target object images , the target object images are subjected to image recognition analysis so as to classify the target objects , decoders that correspond to types of the classified target objects are automatically driven so that the target object images are decoded and target object information is obtained , additional data that are related to augmented reality are obtained , and the additional data and the target object information are sent to a server . The system further comprises the server . The present invention relates to an industrial drawing encryption system based on an image of an intelligent mobile terminal and an encryption method of the system . The intelligent mobile terminal comprises a central processing module and modules respectively connected to the central processing module , such as a fingerprint acquisition processing module , an image acquisition module , an image storage module , an image extraction module , an image type recognition module , an image type conversion module , an image pre-processing module , a cryptographic module , a positioning module , a private key generation module , a text recognition conversion module , an image transmission module and a private information extraction module ; the image storage module is connected to the image acquisition module and the image extraction module ; the image pre-processing module is connected to the image type recognition module , the image type conversion module and the cryptographic module ; the positioning module is connected to the private key generation module ; the text recognition conversion module is connected to the cryptographic module ; the image transmission module is connected to the cryptographic module , the private key generation module and the private information extraction module ; and not only can a legal identity of a mobile terminal operator be identified , but also private information can be safely embedded into an industrial drawing . The first is the VGG architecture of . Compared to the networks used previously in image recognition , this network uses small ( 3x3 ) filters , is deeper , and applies up to five convolutional layers before pooling . First , in this work we would like to focus on analyzing the effectiveness and performance of the aggregation module with the attention mechanism . Despite the huge success of applying deep CNN in image - based face recognition task , little attention has been drawn to CNN feature aggregation to our knowledge . Second , training a deep CNN usually necessitates a large volume of labeled data . Convolutional neural networks ( CNNs ) are a special type of artificial neural networks that learn a hierarchy of increasingly complex features by successive convolution , pooling and non - linear activation operations . Originally designed for image recognition and classification , CNNs are now commonly used in semantic image segmentation . A naive approach follows a sliding - window strategy , where regions defined by the window are processed independently . We re - evaluate the state of the art for object recognition from small images with convolutional networks , questioning the necessity of different components in the pipeline . We find that max - pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks . Following this finding – and building on other recent work for finding simple network structures – we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets ( CIFAR - 10 , CIFAR - 100 , ImageNet ) . The whole Bayesian formulation is solved in an Expectation-Maximization paradigm . After finding those discriminative patterns among different images , we design an image categorization algorithm to interpret how these differences help visual recognition within the bag-of-feature framework . The proposed method is verified on a variety of image categorization tasks including outdoor scene images , indoor scene images as well as the airborne SAR images from different perspectives . Our method allows to deal with different scenarii , where isolated images or groups of images are used for training and testing . The results obtained on an image categorization problem comprising of 3849 images from 101 landmarks of Singapore show that ( a ) the procedure to automatically induce concepts from an image is effective , and ( b ) the use of spatial relationships , in addition to concepts , for representing an image content helps improve the classifier accuracy . This approach is the first one , to our knowledge , to present a complete extension of the language modeling approach from information retrieval to the problem of graph-based image categorization and retrieval . Image categorization research has created knowledge on the types of attributes humans use in interpreting image similarity . Little effort has gone into studying the effect of any wider contextual factors on the image groupings people create . There is a core problem about how to auto-recognize and cluster images captured by digital cameras in camera auto-adjusted mode . A new supervised learning algorithm based-on visual characters and LDA is presented , which applies kinds of image codewords which include visual characters of images as training units prepared for image categorizing . After the auto-recognition system being trained , different digital pictures can be clustered and then their matched modes will be chosen for terminal users . We have performed these user interaction properties and photo properties analysis task on eight major cities ’ Instagram posts and further classified the posts of these eight cities in five categories using Non-negative matrix factorization and latent Dirichlet allocation algorithm . The four prime influencing analyses have been computed to get ecology of the users on Instagram photo posts , which are Time based analysis ( TBA ) , Image Filter analysis ( IFA ) , Image Hashtags analysis ( IHA ) and Image categorization analysis ( ICA ) . Henceforth , this multivariate feature based Instagram analysis will help users to gain insight of popular content and make their respective content popular so as to reach out to a maximum number of people . The patent image classification task consisted in categorizing patent images into predefined categories such as abstract drawing , graph , flowchart , table , etc . Our main aim in participating in this sub-task was to test how our image categorizer performs on this type of categorization problem . Therefore , we used SIFT-like local orientation histograms as low level features and on the top of that we built a visual vocabularies specific to patent images using Gaussian mixture model ( GMM ) . In the following , we present a synopsis of papers collected in his special issue , which address some aspects of the above reearch problems . More specifically , some papers study challenging ssues , such as sparse representation , dictionary learning , transfer earning and unsupervised learning , in machine learning and attern recognition , while others target at solving real-world probems , such as image classification/categorization , motion detection , edical diagnosis , web ranking , natural language modeling , huan pose estimation , image inpainting , video quality assessment nd so on . Deep neural networks paved the way for significant improvements in image visual categorization during the last years . However , even though the tasks are highly varying , differing in complexity and difficulty , existing solutions mostly build on the same architectural decisions . Most recent methods are based on the idea of a joint embedding of the image and the question using a deep neural network . The image is passed through a convolutional neural network ( CNN ) pretrained for image classification , from which intermediate features are extracted to describe the image . The question is typically passed through a recurrent neural network ( RNN ) such as an LSTM , which produces a fixed - size vector representing the sequence of words . Image classification and object detection are two fundamental computer vision tasks . Object detectors are often trained through a reduction that converts object detection into an image classification problem . This reduction introduces a new challenge that is not found in natural image classification tasks : the training set is distinguished by a large imbalance between the number of annotated objects and the number of background examples ( image regions not belonging to any object class of interest ) . In these methods , new images are synthesized by minimizing the content loss with respect to one input sample and the style loss with respect to one or more input samples . The content loss is typically the encoding of the image by a network training for an image categorization task , similar to our work . The style loss compares the statistics of the activations in various layers of the neural network . Trained as an image classifier , the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data . The model , which is a simple and elegant end - to - end architecture , outperforms standard data augmentation and fine - tuning techniques for the task of image - level classification as well . section : Introduction Land cover/use mapping at national level using visual classification has been widely accepted in the developing world due to its high classification accuracy ( > 85 % ) . However this visual approach is time consuming and most countries can ill afford to update their database regularly . Recent research in visual data classification often involves image sets and the measurement of dissimilarity between each pair of them . An effective solution is to model each image set using a subspace and compute the distance between these two subspaces as the dissimilarity between the sets . The visual retrieval from broad image collections such as teaching files , the medical literature or web images , by contrast , has not yet reached a high maturity level compared to textual information retrieval . Visual image classification into a relatively small number of classes ( 20 - 100 ) on the other hand , has shown to deliver good results in several benchmarks . It is , however , currently underused as a basic technology for retrieval tasks , for example , to limit the search space . A particular one is automatic detection of parking lot occupancy , where a computer has to identify whether a parking lot space is empty or occupied . As in any visual classification problem , detecting parking lot spaces relies on the existence of a representative visual dataset . This problem of binary classification is commonly approached using features with adequate level of invariance to changes in illumination or rotation , that allow feeding these features into classifiers such as the SVM . The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion . Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision . This work will enable the rover to drive safely on slopes , learning autonomously about different terrains and their slip characteristics . This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images . The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures . While solely based on time-frequency texture features , the algorithm achieves surprisingly good performance in musical instrument classification experiments . A Preliminary Study to Map Users ' Search Behaviour.- Identification of Bibliographic Information Written in Both Japanese and English.- DIGMAP : A Digital Library Reusing Metadata of Old Maps and Enriching It with Geographic Information.- Visual Analysis of Classification Systems and Library Collections.- A Framework for Music Content Description and Retrieval.- XCL : Compared with traditional uncertainty-based active sampling strategy , the proposed sample selection framework consists of both a certainty metric and sample postprocessing for more precise evaluation . The certainty metric is measured by the visual classifying model , and the postprocessing module is implemented by the Q-learning algorithm to construct a compact training set for the visual module to further improve the effectiveness and efficiency of classification . Meanwhile , the parameters of the whole sampling framework are calculated adaptively instead of being set manually to improve the adaptiveness of the whole framework . Convolutional neural network has different semantic information and spatial resolution at different feature layers . The shallow layers usually have high spatial resolution , which is good for spatial localization of small objects , but low semantic information , which is not good for visual classification . On the other hand , deep layers obtain more semantic information but the spatial resolution is compromised . section : Introduction Recently , attention - based recurrent networks have been successfully applied to a wide variety of tasks , such as handwriting synthesis , machine translation , image caption generation and visual object classification . Such models iteratively process their input by selecting relevant content at every step . Our model handles label-related and -unrelated topics explicitly , in contrast to the earlier model , and improves the performances of applications to which is applied . Using real-world datasets , we show that our model outperforms the earlier model in terms of perplexity and efficiency for label prediction tasks that involve predicting labels for documents or pictures without labels . The techniques of classification are through learning historical data to help people to predict the class label of data , and they have been applied to solve many problems . In the real world , there exists many sequence data , such as genome sequences , those should be learned and analyzed for predicting class labels . The traditional classification methods are unsuitable for sequence data . Similar classification performance was observed in the analyses of the repeated subjective rating . Current results served as a proof of concept for predicting images that fail quality control using no‐reference objective image features . We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images . In addition to identifying the content within a single image , relating images and generating related images are critical tasks for image understanding . Recently , deep convolutional networks have yielded breakthroughs in predicting image labels , annotations and captions , but have only just begun to be used for generating high-quality images . In this paper we develop a novel deep network trained end-to-end to perform visual analogy making , which is the task of transforming a query image according to an example pair of related images . This article presents a user-centric system , called iScope , for personal image management and sharing on mobile devices . iScope uses multi-modality clustering of both content and context information for efficient image management and search , and online learning techniques for predicting images of interest . It also supports distributed image search among networked devices while maintaining the same intuitive interface , enabling efficient information sharing among people . Given sample data and background knowledge encoded in the form of logic programs , a predictive Inductive Logic Programming ( ILP ) system attempts to find a set of rules ( or clauses ) for predicting classification labels in the data . Most present-day systems for this purpose rely on some variant of a generate-and-test procedure that repeatedly examines a set of potential candidates ( termed here as the “ hypothesis space ” ) . I. Introduction Object prediction represents an important field of predicting image objects , based on their shape information . Prediction of the partly cover and coupled objects accurately , leads to the reduced execution time and elapsed time . While bottom-up and top-down processes have shown effectiveness during predicting attention and eye fixation maps on images , in this paper , inspired by the perceptual organization mechanism before attention selection , we propose to utilize figure-ground maps for the purpose . So as to take both pixel-wise and region-wise interactions into consideration when predicting label probabilities for each pixel , we develop a context-aware model based on multiple segmentation to obtain final results . The MIT attention dataset [ 14 ] is applied finally to evaluate both new features and model . The main advantage of this approach is that it provides faster interactive labeling rates than alternative approaches , especially in cases where all labels must be explicitly confirmed by a person . The main challenge is to provide an algorithm that selects groups with many objects all of the same label type arranged in patterns that are quick to recognize , which requires models for predicting object labels and for estimating times for people to recognize objects in groups . We address these challenges by defining an objective function that models the estimated time required to process all unlabeled objects and approximation algorithms to minimize it . section : Introduction Ordinal regression , sometimes also referred to as ordinal classification , describes the task of predicting object labels on an ordinal scale . Here , a ranking rule or classifier maps each object into an ordered set , where .
Within the statistical decision theory framework , This paper focuses on specific objectives in face recognition , and proposes the BP neural network classification methods which is under the Linex loss function , and proves that the convergence of BP neural network under the Linex loss function . The image recognition experiments with the ORL face database in Cambridge show that this method can effectively solve the traditional problems in specific target face recognition of BP neural network .
Two different implementations are described ; one using an exact line search to find the minimum of the error along the current search direction , the other avoids the line search by controlling the positive indefiniteness of the Hessian matrix . The two implementations are compared and evaluated in the context of an image recognition problem using input bit-maps with a resolution of 128 by 128 pixels .
In this method , the neural network responds in real-time . This is connected with reorganizing classical paradigm of image recognition process . The character of recognition stage is performed before the image segmentation .
2009 Thesis directed by : Professor Rama Chellappa Department of Electrical and Computer Engineering Recently , face recognition based on video has gained wide interest especially due to its role in surveillance systems . Video-based recognition has superior advantages over image-based recognition because a video contains image sequences as well as temporal information . However , surveillance videos are generally of lowresolution and contain faces mostly in non-frontal poses .
The invention provides a ground object information collection device and a program thereof , a self-driving position recognition device and a navigation device , which can quickly collect the ground object information after construction when the ground object position shifts or the ground object category alters owing to construction . The devices are provided with a ground object image recognition unit carrying out image recognition processing to the ground objects included in the image information of the vehicle surroundings , a construction information acquisition unit for acquiring the construction information in the construction zone , a construction storage unit for storing the acquired construction information , a construction completion judging unit for judging whether the construction indicated by the construction information completes when the vehicle drives on the roads corresponding to the constriction zone related to the stored construction information , and a ground object study unit for enabling the ground object recognition unit to carry out image recognition processing to the ground object in the construction zone related to the construction information after the construction is judged to complete and generating study ground object information including the position information and attribution information of the recognized ground object based on the image recognition result and the vehicle position information .
It is of great importance to investigate the domain adaptation problem of image object recognition , because now image data is available from a variety of source domains . To understand the changes in data distributions across domains , we study both the input and output kernel spaces for cross-domain learning situations , where most labeled training images are from a source domain and testing images are from a different target domain .
He has over 30 published papers and/or technical presentations while spearheading over 40 international scientific and engineering conferences/workshops as a steering committee member while assigned in Europe . Professor Santiago has experience in many engineering disciplines and missions including : control and modeling of large flexible space structures , communications system , electro-optics , high-energy lasers , missile seekers/sensors for precision guided munitions , image processing/recognition , information technologies , space , air and missile warning , missile defense , and homeland defense .
The system comprises a terminal device . By means of the terminal device , target objects are captured to establish target object images , the target object images are subjected to image recognition analysis so as to classify the target objects , decoders that correspond to types of the classified target objects are automatically driven so that the target object images are decoded and target object information is obtained , additional data that are related to augmented reality are obtained , and the additional data and the target object information are sent to a server . The system further comprises the server .
The present invention relates to an industrial drawing encryption system based on an image of an intelligent mobile terminal and an encryption method of the system . The intelligent mobile terminal comprises a central processing module and modules respectively connected to the central processing module , such as a fingerprint acquisition processing module , an image acquisition module , an image storage module , an image extraction module , an image type recognition module , an image type conversion module , an image pre-processing module , a cryptographic module , a positioning module , a private key generation module , a text recognition conversion module , an image transmission module and a private information extraction module ; the image storage module is connected to the image acquisition module and the image extraction module ; the image pre-processing module is connected to the image type recognition module , the image type conversion module and the cryptographic module ; the positioning module is connected to the private key generation module ; the text recognition conversion module is connected to the cryptographic module ; the image transmission module is connected to the cryptographic module , the private key generation module and the private information extraction module ; and not only can a legal identity of a mobile terminal operator be identified , but also private information can be safely embedded into an industrial drawing .
The first is the VGG architecture of . Compared to the networks used previously in image recognition , this network uses small ( 3x3 ) filters , is deeper , and applies up to five convolutional layers before pooling .
First , in this work we would like to focus on analyzing the effectiveness and performance of the aggregation module with the attention mechanism . Despite the huge success of applying deep CNN in image - based face recognition task , little attention has been drawn to CNN feature aggregation to our knowledge . Second , training a deep CNN usually necessitates a large volume of labeled data .
Convolutional neural networks ( CNNs ) are a special type of artificial neural networks that learn a hierarchy of increasingly complex features by successive convolution , pooling and non - linear activation operations . Originally designed for image recognition and classification , CNNs are now commonly used in semantic image segmentation . A naive approach follows a sliding - window strategy , where regions defined by the window are processed independently .
We re - evaluate the state of the art for object recognition from small images with convolutional networks , questioning the necessity of different components in the pipeline . We find that max - pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks . Following this finding – and building on other recent work for finding simple network structures – we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets ( CIFAR - 10 , CIFAR - 100 , ImageNet ) .
The whole Bayesian formulation is solved in an Expectation-Maximization paradigm . After finding those discriminative patterns among different images , we design an image categorization algorithm to interpret how these differences help visual recognition within the bag-of-feature framework . The proposed method is verified on a variety of image categorization tasks including outdoor scene images , indoor scene images as well as the airborne SAR images from different perspectives .
Our method allows to deal with different scenarii , where isolated images or groups of images are used for training and testing . The results obtained on an image categorization problem comprising of 3849 images from 101 landmarks of Singapore show that ( a ) the procedure to automatically induce concepts from an image is effective , and ( b ) the use of spatial relationships , in addition to concepts , for representing an image content helps improve the classifier accuracy . This approach is the first one , to our knowledge , to present a complete extension of the language modeling approach from information retrieval to the problem of graph-based image categorization and retrieval .
Image categorization research has created knowledge on the types of attributes humans use in interpreting image similarity . Little effort has gone into studying the effect of any wider contextual factors on the image groupings people create .
There is a core problem about how to auto-recognize and cluster images captured by digital cameras in camera auto-adjusted mode . A new supervised learning algorithm based-on visual characters and LDA is presented , which applies kinds of image codewords which include visual characters of images as training units prepared for image categorizing . After the auto-recognition system being trained , different digital pictures can be clustered and then their matched modes will be chosen for terminal users .
We have performed these user interaction properties and photo properties analysis task on eight major cities ’ Instagram posts and further classified the posts of these eight cities in five categories using Non-negative matrix factorization and latent Dirichlet allocation algorithm . The four prime influencing analyses have been computed to get ecology of the users on Instagram photo posts , which are Time based analysis ( TBA ) , Image Filter analysis ( IFA ) , Image Hashtags analysis ( IHA ) and Image categorization analysis ( ICA ) . Henceforth , this multivariate feature based Instagram analysis will help users to gain insight of popular content and make their respective content popular so as to reach out to a maximum number of people .
The patent image classification task consisted in categorizing patent images into predefined categories such as abstract drawing , graph , flowchart , table , etc . Our main aim in participating in this sub-task was to test how our image categorizer performs on this type of categorization problem . Therefore , we used SIFT-like local orientation histograms as low level features and on the top of that we built a visual vocabularies specific to patent images using Gaussian mixture model ( GMM ) .
In the following , we present a synopsis of papers collected in his special issue , which address some aspects of the above reearch problems . More specifically , some papers study challenging ssues , such as sparse representation , dictionary learning , transfer earning and unsupervised learning , in machine learning and attern recognition , while others target at solving real-world probems , such as image classification/categorization , motion detection , edical diagnosis , web ranking , natural language modeling , huan pose estimation , image inpainting , video quality assessment nd so on .
Deep neural networks paved the way for significant improvements in image visual categorization during the last years . However , even though the tasks are highly varying , differing in complexity and difficulty , existing solutions mostly build on the same architectural decisions .
Most recent methods are based on the idea of a joint embedding of the image and the question using a deep neural network . The image is passed through a convolutional neural network ( CNN ) pretrained for image classification , from which intermediate features are extracted to describe the image . The question is typically passed through a recurrent neural network ( RNN ) such as an LSTM , which produces a fixed - size vector representing the sequence of words .
Image classification and object detection are two fundamental computer vision tasks . Object detectors are often trained through a reduction that converts object detection into an image classification problem . This reduction introduces a new challenge that is not found in natural image classification tasks : the training set is distinguished by a large imbalance between the number of annotated objects and the number of background examples ( image regions not belonging to any object class of interest ) .
In these methods , new images are synthesized by minimizing the content loss with respect to one input sample and the style loss with respect to one or more input samples . The content loss is typically the encoding of the image by a network training for an image categorization task , similar to our work . The style loss compares the statistics of the activations in various layers of the neural network .
Trained as an image classifier , the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data . The model , which is a simple and elegant end - to - end architecture , outperforms standard data augmentation and fine - tuning techniques for the task of image - level classification as well . section : Introduction
Land cover/use mapping at national level using visual classification has been widely accepted in the developing world due to its high classification accuracy ( > 85 % ) . However this visual approach is time consuming and most countries can ill afford to update their database regularly .
Recent research in visual data classification often involves image sets and the measurement of dissimilarity between each pair of them . An effective solution is to model each image set using a subspace and compute the distance between these two subspaces as the dissimilarity between the sets .
The visual retrieval from broad image collections such as teaching files , the medical literature or web images , by contrast , has not yet reached a high maturity level compared to textual information retrieval . Visual image classification into a relatively small number of classes ( 20 - 100 ) on the other hand , has shown to deliver good results in several benchmarks . It is , however , currently underused as a basic technology for retrieval tasks , for example , to limit the search space .
A particular one is automatic detection of parking lot occupancy , where a computer has to identify whether a parking lot space is empty or occupied . As in any visual classification problem , detecting parking lot spaces relies on the existence of a representative visual dataset . This problem of binary classification is commonly approached using features with adequate level of invariance to changes in illumination or rotation , that allow feeding these features into classifiers such as the SVM .
The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion . Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision . This work will enable the rover to drive safely on slopes , learning autonomously about different terrains and their slip characteristics .
This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images . The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures . While solely based on time-frequency texture features , the algorithm achieves surprisingly good performance in musical instrument classification experiments .
A Preliminary Study to Map Users ' Search Behaviour.- Identification of Bibliographic Information Written in Both Japanese and English.- DIGMAP : A Digital Library Reusing Metadata of Old Maps and Enriching It with Geographic Information.- Visual Analysis of Classification Systems and Library Collections.- A Framework for Music Content Description and Retrieval.- XCL :
Compared with traditional uncertainty-based active sampling strategy , the proposed sample selection framework consists of both a certainty metric and sample postprocessing for more precise evaluation . The certainty metric is measured by the visual classifying model , and the postprocessing module is implemented by the Q-learning algorithm to construct a compact training set for the visual module to further improve the effectiveness and efficiency of classification . Meanwhile , the parameters of the whole sampling framework are calculated adaptively instead of being set manually to improve the adaptiveness of the whole framework .
Convolutional neural network has different semantic information and spatial resolution at different feature layers . The shallow layers usually have high spatial resolution , which is good for spatial localization of small objects , but low semantic information , which is not good for visual classification . On the other hand , deep layers obtain more semantic information but the spatial resolution is compromised .
section : Introduction Recently , attention - based recurrent networks have been successfully applied to a wide variety of tasks , such as handwriting synthesis , machine translation , image caption generation and visual object classification . Such models iteratively process their input by selecting relevant content at every step .
Our model handles label-related and -unrelated topics explicitly , in contrast to the earlier model , and improves the performances of applications to which is applied . Using real-world datasets , we show that our model outperforms the earlier model in terms of perplexity and efficiency for label prediction tasks that involve predicting labels for documents or pictures without labels .
The techniques of classification are through learning historical data to help people to predict the class label of data , and they have been applied to solve many problems . In the real world , there exists many sequence data , such as genome sequences , those should be learned and analyzed for predicting class labels . The traditional classification methods are unsuitable for sequence data .
Similar classification performance was observed in the analyses of the repeated subjective rating . Current results served as a proof of concept for predicting images that fail quality control using no‐reference objective image features . We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images .
In addition to identifying the content within a single image , relating images and generating related images are critical tasks for image understanding . Recently , deep convolutional networks have yielded breakthroughs in predicting image labels , annotations and captions , but have only just begun to be used for generating high-quality images . In this paper we develop a novel deep network trained end-to-end to perform visual analogy making , which is the task of transforming a query image according to an example pair of related images .
This article presents a user-centric system , called iScope , for personal image management and sharing on mobile devices . iScope uses multi-modality clustering of both content and context information for efficient image management and search , and online learning techniques for predicting images of interest . It also supports distributed image search among networked devices while maintaining the same intuitive interface , enabling efficient information sharing among people .
Given sample data and background knowledge encoded in the form of logic programs , a predictive Inductive Logic Programming ( ILP ) system attempts to find a set of rules ( or clauses ) for predicting classification labels in the data . Most present-day systems for this purpose rely on some variant of a generate-and-test procedure that repeatedly examines a set of potential candidates ( termed here as the “ hypothesis space ” ) .
I. Introduction Object prediction represents an important field of predicting image objects , based on their shape information . Prediction of the partly cover and coupled objects accurately , leads to the reduced execution time and elapsed time .
While bottom-up and top-down processes have shown effectiveness during predicting attention and eye fixation maps on images , in this paper , inspired by the perceptual organization mechanism before attention selection , we propose to utilize figure-ground maps for the purpose . So as to take both pixel-wise and region-wise interactions into consideration when predicting label probabilities for each pixel , we develop a context-aware model based on multiple segmentation to obtain final results . The MIT attention dataset [ 14 ] is applied finally to evaluate both new features and model .
The main advantage of this approach is that it provides faster interactive labeling rates than alternative approaches , especially in cases where all labels must be explicitly confirmed by a person . The main challenge is to provide an algorithm that selects groups with many objects all of the same label type arranged in patterns that are quick to recognize , which requires models for predicting object labels and for estimating times for people to recognize objects in groups . We address these challenges by defining an objective function that models the estimated time required to process all unlabeled objects and approximation algorithms to minimize it .
section : Introduction Ordinal regression , sometimes also referred to as ordinal classification , describes the task of predicting object labels on an ordinal scale . Here , a ranking rule or classifier maps each object into an ordered set , where .
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IEA/AIE 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Comput. Medical Imaging Graph. ICLR CBMI ASIST ITQM CLEF (Notebook Papers/Labs/Workshop) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISCAS WEA (1) ICRA ICASSP ArXiv NIPS ACPR NIPS CVPR UIST ArXiv
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