Small-sample learning in image retrieval is a pertinent and interesting problem . Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples . Investigational outcomes display that maximum 40 % comparative progress has been attained on re-ranking precisions equated with state of art methodologies . Automated indexing and text alignment with similar image clustering adds improved technique to IIR ( image information retrieval ) . The research further implements incremental learning framework . Color-based object recognition is typically concerned with building statistical descriptions from pixels that correspond to an object class and then using these models to detect pixels that belong to previously seen objects . Specific instances of color-based classification occur in a number of computer vision problems including background modeling , image-based retrieval , and multi-view object recognition and tracking . Color-based models are dependent on the intrinsic parameters of the camera(s ) used to acquire them . The inverted index can require gigabytes of memory , which significantly slows down the database server . In this paper , we design , develop , and compare techniques for inverted index compression for image-based retrieval . We show that these techniques significantly reduce memory usage , by as much as 5x , without loss in recognition accuracy . For the improvement of the capacity of image query retrieval used image classification technique , 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 . It is expected that K should n't be too small so that underfitting is avoided ( e.g. K = 1 is similar to average - pooling ) nor too large in order to prevent over - quantization and overfitting . As in traditional image retrieval [ reference ] , we find that a wide range of K achieves good performance , with K = 8 being the best . Ghost clusters . Between these four models , the only previous work whose results are incommensurable with ours is DVSA , since it uses the less discriminative CNN of but 20 region features instead of a single whole - image feature . Aside from this limitation , and if only single models are considered , order - embeddings significantly outperform the state - of - art approaches for image retrieval even when we control for image features . subsection : Exploration We rank the images in the test set conditioned on the captions based on the variational lower bound of the log - probabilities and then report the Precision - Recall metric as an evaluation of the quality of the generative model ( see Table [ reference ] . ) . Perhaps unsurprisingly , generative models did not perform well on the image retrieval task . To deal with the large computational complexity involved in looping through each test image , we create a shortlist of one hundred images including the correct one , based on the images having the closest Euclidean distance in the last fully - connected feature space of a VGG - like model simonyan_convnet trained on the CIFAR dataset krizhevsky_cifar . Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition , face tracking , face identification , expression recognition and also content-based image retrieval . A potent face detection system must be able to detect faces irrespective of illuminations , shadows , cluttered backgrounds , orientation and facial expressions . A novel indexing scheme for solving the problem of nearest neighbor queries in generic metric feature spaces for content-based image retrieval is proposed to break the " dimensionality curse . " The basis for the proposed method is the partitioning of the feature dataset into clusters that are represented by single buoys . This paper emphasis on spatial feature extractions and selection techniques adopted in content based image retrieval that uses the visual content of a still image to search for similar images in large scale image databases , according to a user ’s interest . The content based image retrieval problem is motivated by the need to search the exponentially increasing space of image databases efficiently and effectively . It is also possible to classify the remotely sensed image to represent the specific feature of the target images . Evaluation shows images judged to be similar by human form interesting clusters in our unsupervised learning . Comparison with other clustering technique , such as Hierarchical Agglomerative Clustering ( HAC ) , demonstrates that our approach is found useful in content-based image information retrieval . In image information retrieval , an important issue is how to index image objects , so that the visual objects can be accessed quickly and certain actions can be performed automatically . This chapter introduces the active index for content-based image information retrieval . The dynamic nature of the active index is its most important characteristic . Texture likelihood research is an important part of the content-based image retrieval research . Adoptes wavelet Daubechies4 to conduct a three level Mallat pyramid decomposition , takes the energy of each subband image in each decomposition level and adds in the gray level co-occurrence matrix statistics extracted in the low-frequency subband to become the final feature vector in order to increase the retrieval accuracy . In content-based visual image retrieval , image representation is one of the fundamental issues in improving retrieval performance . Recently Convolutional Neural Network ( CNN ) features have shown their great success as a universal representation . By adopting a user-centred approach with techniques grounded in contextual design , we identified the phases of archaeologists work in online archives , which are distinctive to this user group . The insights from this work drove the design and evaluation of an interactive system that successfully integrates content-based image based retrieval and improved metadata searching to deliver a positive user experience when working with online archives . In this paper , we proposed a new type of image feature , which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are eliminated . We introduced the synobin , a new term for content-based image retrieval literature , which is the equivalent of a synonym word from text retrieval , to name the bin that is synonymous with other bins of a color feature , in the sense that they are similarly used across the image database . In a formal definition , a group of synobins is given by the most important bins participating in forming of a useful pattern , that is , the bins having the highest coefficients in the linear combination defining that pattern . It is useful in many image processing applications . In content based image retrieval searching are based on the comparision of the query image and it is working on the whole image . Color , texture and shape are the important feature in the image retrieval system . Person re - identification aims to search for the same person across different cameras with a given probe image . It has attracted much attention in recent years due to its importance in many practical applications , such as video surveillance and content - based image retrieval . Despite of years of efforts , it still has many challenges , such as large variations in person pose , illumination , and background clutter . They are easily recognized across cultures and suitable for various applications . In this paper , we use deep convolutional neural networks ( ConvNets ) to address sketch-based image retrieval ( SBIR ) . We first train our ConvNets on sketch and image object recognition in a large scale benchmark for SBIR ( the sketchy database ) . Sketch based image retrieval is a particular case of the image retrieval problem , in which a query is not a regular example image . Instead , the query is a hand-drawn sketch representing what the user is looking for . We describe a visual editor , capable of storing , indexing , retrieving and reusing behaviours previously designed by AI programmers . One of the most notable features of our editor is its capability for sketch-based retrieval : searching in a repository for behaviours that are similar to the one the user is drawing , and making suggestions about how to complete it . As this process relies on graph behaviour comparison , in this paper , we describe different algorithms for graph comparison , and demonstrate , through empirical evaluation in a particular test domain , that we can provide structure-based similarity for graphs that preserves behaviour similarity and can be computed at reasonable cost . As an effective and convenient intermediate means , sketch has been playing an important role in expressing human intentions . Aware of its significance , plentiful sketch related studies have been conducted , such as sketch based image retrieval and recognition . However , very few works have researched the intrinsic factors that make sketch look vivid . To efficiently handle large volumes of data it makes use of a signature-based indexing and the distribution of the collection to multiple shards that are queried in a MapReduce style . We present ADAM in the setting of a sketch-based image retrieval application using the ImageNet collection containing 14 million images . This paper proposes the edge detection for the sketch image retrieval system . An effective edge detection is required for the edge-based sketch image retrieval system , because the retrieval results depend on edge images . This gap is due to noise in edges of photo realistic images . Hence our proposed process considers these issues in Sketch Based Image Retrieval process . Our system eliminates the impact of noise in edges of photo realistic images . For sketch-based retrieval of images , characterization and comparison of the geometrical structure ( shape ) information is of great importance . In this paper , a directional-projection-based sketch image retrieval system ( DPSIR ) is proposed . In this work , we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval ( FG-SBVR ) , where a sketch sequence is used as a query to retrieve a specific target video instance . Compared with sketch-based still image retrieval , and coarse-grained category-level video retrieval , this is more challenging as both visual appearance and motion need to be simultaneously matched at a fine-grained level . We contribute the first FG-SBVR dataset with rich annotations . In the proposed method , vector images are not rasterized , which enables realtime retrieval . As a prototyping , we have implemented a system for sketch-based vector image retrieval . In the system , a sketch is used as a query . The recommended system focus on Bag of features ( Bof ) model in image instance retrieval system . Most of the years , image retrieval is mainly used for browsing and searching for many applications . While the good performance of CNNs for image classification are unambiguously recognised , which of the two has the upper hand in the image retrieval context is not entirely clear yet . We propose a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval . The first part compares the performances of FVs and CNNs on multiple publicly available data sets and for multiple criteria . We show that performance of CNNs can quickly degrade in the presence of certain transformations and propose a number of ways to incorporate the required invariances in the CNN pipeline . Our findings are organised as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem . HighlightsCNNs exhibit very limited invariance to rotation changes compared to FVDoG.CNNs are more robust to scale changes than any variants of FV.Max-pooling across rotated/scaled database images gains rotation/scale invariance . For the organization of image data retrieval systems , one of the most important features to be realized is a large scale semantic data base operating in a real-time environment . This calls for the development of a high-speed associative processor incorporating recent LSI technology . We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets . Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio ( SNR ) of deep convolutional feature maps . We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets . Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio ( SNR ) of deep convolutional feature maps . Image based instance recognition is a difficult problem , in some cases even for the human eye . While latest developments in computer vision — mostly driven by deep learning — have shown that high performance models for classification or categorization can be engineered , the problem of discriminating similar objects with a low number of samples remain challenging . A content based image retrieval system , called MPEG-7 image retrieval refinement based on relevance feedback ( MIRROR ) , is developed for evaluating MPEG-7 visual descriptors and developing new retrieval algorithms . The system core is based on MPEG-7 experimentation mode ( XM ) with Web-based user interface for query by image example retrieval . A new merged color palette approach for MPEG-7 dominant color descriptor similarity measure and relevance feedback are also developed in this system . Deep features extracted from Convolutional Neural Networks ( CNNs ) have proved to have strong ability to transfer for various visual recognition tasks , such as image classification , object detection , fine-grained recognition , and image instance-level retrieval . In this paper , we focus on the issue of high-resolution remote sensing ( HRRS ) image retrieval and take advantage of CNN representations to solve this significant problem . Instead of learning a set of parts per class , as generally performed in the PBM literature , the proposed approach both constructs a partition of a given set of images into visually similar groups , and subsequently learns a set of discriminative parts per group in a fully unsupervised fashion . This strategy opens the door to the use of PBM in new applications where labeled data are typically not available , such as instance-based image retrieval . Second , this paper shows that despite the recent success of end-to-end models , explicit part learning can still boost classification performance . That Shoe . We investigate the problem of fine-grained sketch-based image retrieval ( SBIR ) , where free-hand human sketches are used as queries to perform instance-level retrieval of images . This is an extremely challenging task because ( i ) visual comparisons not only need to be fine-grained but also executed cross-domain , ( ii ) free-hand ( finger ) sketches are highly abstract , making fine-grained matching harder , and most importantly ( iii ) annotated cross-domain sketch-photo datasets required for training are scarce , challenging many state-of-the-art machine learning techniques . Current manifold ranking is mainly used in single-instance image retrieval without considering the prevailing semantic ambiguity problem . This paper introduces multi-instance technique and supervised information to image retrieval based on manifold ranking , and proposes a Multi-label Supervised Manifold Ranking algorithm ( MSMR ) for multi-instance image retrieval .
Small-sample learning in image retrieval is a pertinent and interesting problem . Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples .
Investigational outcomes display that maximum 40 % comparative progress has been attained on re-ranking precisions equated with state of art methodologies . Automated indexing and text alignment with similar image clustering adds improved technique to IIR ( image information retrieval ) . The research further implements incremental learning framework .
Color-based object recognition is typically concerned with building statistical descriptions from pixels that correspond to an object class and then using these models to detect pixels that belong to previously seen objects . Specific instances of color-based classification occur in a number of computer vision problems including background modeling , image-based retrieval , and multi-view object recognition and tracking . Color-based models are dependent on the intrinsic parameters of the camera(s ) used to acquire them .
The inverted index can require gigabytes of memory , which significantly slows down the database server . In this paper , we design , develop , and compare techniques for inverted index compression for image-based retrieval . We show that these techniques significantly reduce memory usage , by as much as 5x , without loss in recognition accuracy .
For the improvement of the capacity of image query retrieval used image classification technique , 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 .
It is expected that K should n't be too small so that underfitting is avoided ( e.g. K = 1 is similar to average - pooling ) nor too large in order to prevent over - quantization and overfitting . As in traditional image retrieval [ reference ] , we find that a wide range of K achieves good performance , with K = 8 being the best . Ghost clusters .
Between these four models , the only previous work whose results are incommensurable with ours is DVSA , since it uses the less discriminative CNN of but 20 region features instead of a single whole - image feature . Aside from this limitation , and if only single models are considered , order - embeddings significantly outperform the state - of - art approaches for image retrieval even when we control for image features . subsection : Exploration
We rank the images in the test set conditioned on the captions based on the variational lower bound of the log - probabilities and then report the Precision - Recall metric as an evaluation of the quality of the generative model ( see Table [ reference ] . ) . Perhaps unsurprisingly , generative models did not perform well on the image retrieval task . To deal with the large computational complexity involved in looping through each test image , we create a shortlist of one hundred images including the correct one , based on the images having the closest Euclidean distance in the last fully - connected feature space of a VGG - like model simonyan_convnet trained on the CIFAR dataset krizhevsky_cifar .
Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition , face tracking , face identification , expression recognition and also content-based image retrieval . A potent face detection system must be able to detect faces irrespective of illuminations , shadows , cluttered backgrounds , orientation and facial expressions .
A novel indexing scheme for solving the problem of nearest neighbor queries in generic metric feature spaces for content-based image retrieval is proposed to break the " dimensionality curse . " The basis for the proposed method is the partitioning of the feature dataset into clusters that are represented by single buoys .
This paper emphasis on spatial feature extractions and selection techniques adopted in content based image retrieval that uses the visual content of a still image to search for similar images in large scale image databases , according to a user ’s interest . The content based image retrieval problem is motivated by the need to search the exponentially increasing space of image databases efficiently and effectively . It is also possible to classify the remotely sensed image to represent the specific feature of the target images .
Evaluation shows images judged to be similar by human form interesting clusters in our unsupervised learning . Comparison with other clustering technique , such as Hierarchical Agglomerative Clustering ( HAC ) , demonstrates that our approach is found useful in content-based image information retrieval .
In image information retrieval , an important issue is how to index image objects , so that the visual objects can be accessed quickly and certain actions can be performed automatically . This chapter introduces the active index for content-based image information retrieval . The dynamic nature of the active index is its most important characteristic .
Texture likelihood research is an important part of the content-based image retrieval research . Adoptes wavelet Daubechies4 to conduct a three level Mallat pyramid decomposition , takes the energy of each subband image in each decomposition level and adds in the gray level co-occurrence matrix statistics extracted in the low-frequency subband to become the final feature vector in order to increase the retrieval accuracy .
In content-based visual image retrieval , image representation is one of the fundamental issues in improving retrieval performance . Recently Convolutional Neural Network ( CNN ) features have shown their great success as a universal representation .
By adopting a user-centred approach with techniques grounded in contextual design , we identified the phases of archaeologists work in online archives , which are distinctive to this user group . The insights from this work drove the design and evaluation of an interactive system that successfully integrates content-based image based retrieval and improved metadata searching to deliver a positive user experience when working with online archives .
In this paper , we proposed a new type of image feature , which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are eliminated . We introduced the synobin , a new term for content-based image retrieval literature , which is the equivalent of a synonym word from text retrieval , to name the bin that is synonymous with other bins of a color feature , in the sense that they are similarly used across the image database . In a formal definition , a group of synobins is given by the most important bins participating in forming of a useful pattern , that is , the bins having the highest coefficients in the linear combination defining that pattern .
It is useful in many image processing applications . In content based image retrieval searching are based on the comparision of the query image and it is working on the whole image . Color , texture and shape are the important feature in the image retrieval system .
Person re - identification aims to search for the same person across different cameras with a given probe image . It has attracted much attention in recent years due to its importance in many practical applications , such as video surveillance and content - based image retrieval . Despite of years of efforts , it still has many challenges , such as large variations in person pose , illumination , and background clutter .
They are easily recognized across cultures and suitable for various applications . In this paper , we use deep convolutional neural networks ( ConvNets ) to address sketch-based image retrieval ( SBIR ) . We first train our ConvNets on sketch and image object recognition in a large scale benchmark for SBIR ( the sketchy database ) .
Sketch based image retrieval is a particular case of the image retrieval problem , in which a query is not a regular example image . Instead , the query is a hand-drawn sketch representing what the user is looking for .
We describe a visual editor , capable of storing , indexing , retrieving and reusing behaviours previously designed by AI programmers . One of the most notable features of our editor is its capability for sketch-based retrieval : searching in a repository for behaviours that are similar to the one the user is drawing , and making suggestions about how to complete it . As this process relies on graph behaviour comparison , in this paper , we describe different algorithms for graph comparison , and demonstrate , through empirical evaluation in a particular test domain , that we can provide structure-based similarity for graphs that preserves behaviour similarity and can be computed at reasonable cost .
As an effective and convenient intermediate means , sketch has been playing an important role in expressing human intentions . Aware of its significance , plentiful sketch related studies have been conducted , such as sketch based image retrieval and recognition . However , very few works have researched the intrinsic factors that make sketch look vivid .
To efficiently handle large volumes of data it makes use of a signature-based indexing and the distribution of the collection to multiple shards that are queried in a MapReduce style . We present ADAM in the setting of a sketch-based image retrieval application using the ImageNet collection containing 14 million images .
This paper proposes the edge detection for the sketch image retrieval system . An effective edge detection is required for the edge-based sketch image retrieval system , because the retrieval results depend on edge images .
This gap is due to noise in edges of photo realistic images . Hence our proposed process considers these issues in Sketch Based Image Retrieval process . Our system eliminates the impact of noise in edges of photo realistic images .
For sketch-based retrieval of images , characterization and comparison of the geometrical structure ( shape ) information is of great importance . In this paper , a directional-projection-based sketch image retrieval system ( DPSIR ) is proposed .
In this work , we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval ( FG-SBVR ) , where a sketch sequence is used as a query to retrieve a specific target video instance . Compared with sketch-based still image retrieval , and coarse-grained category-level video retrieval , this is more challenging as both visual appearance and motion need to be simultaneously matched at a fine-grained level . We contribute the first FG-SBVR dataset with rich annotations .
In the proposed method , vector images are not rasterized , which enables realtime retrieval . As a prototyping , we have implemented a system for sketch-based vector image retrieval . In the system , a sketch is used as a query .
The recommended system focus on Bag of features ( Bof ) model in image instance retrieval system . Most of the years , image retrieval is mainly used for browsing and searching for many applications .
While the good performance of CNNs for image classification are unambiguously recognised , which of the two has the upper hand in the image retrieval context is not entirely clear yet . We propose a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval . The first part compares the performances of FVs and CNNs on multiple publicly available data sets and for multiple criteria .
We show that performance of CNNs can quickly degrade in the presence of certain transformations and propose a number of ways to incorporate the required invariances in the CNN pipeline . Our findings are organised as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem . HighlightsCNNs exhibit very limited invariance to rotation changes compared to FVDoG.CNNs are more robust to scale changes than any variants of FV.Max-pooling across rotated/scaled database images gains rotation/scale invariance .
For the organization of image data retrieval systems , one of the most important features to be realized is a large scale semantic data base operating in a real-time environment . This calls for the development of a high-speed associative processor incorporating recent LSI technology .
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets . Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio ( SNR ) of deep convolutional feature maps .
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets . Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio ( SNR ) of deep convolutional feature maps .
Image based instance recognition is a difficult problem , in some cases even for the human eye . While latest developments in computer vision — mostly driven by deep learning — have shown that high performance models for classification or categorization can be engineered , the problem of discriminating similar objects with a low number of samples remain challenging .
A content based image retrieval system , called MPEG-7 image retrieval refinement based on relevance feedback ( MIRROR ) , is developed for evaluating MPEG-7 visual descriptors and developing new retrieval algorithms . The system core is based on MPEG-7 experimentation mode ( XM ) with Web-based user interface for query by image example retrieval . A new merged color palette approach for MPEG-7 dominant color descriptor similarity measure and relevance feedback are also developed in this system .
Deep features extracted from Convolutional Neural Networks ( CNNs ) have proved to have strong ability to transfer for various visual recognition tasks , such as image classification , object detection , fine-grained recognition , and image instance-level retrieval . In this paper , we focus on the issue of high-resolution remote sensing ( HRRS ) image retrieval and take advantage of CNN representations to solve this significant problem .
Instead of learning a set of parts per class , as generally performed in the PBM literature , the proposed approach both constructs a partition of a given set of images into visually similar groups , and subsequently learns a set of discriminative parts per group in a fully unsupervised fashion . This strategy opens the door to the use of PBM in new applications where labeled data are typically not available , such as instance-based image retrieval . Second , this paper shows that despite the recent success of end-to-end models , explicit part learning can still boost classification performance .
That Shoe . We investigate the problem of fine-grained sketch-based image retrieval ( SBIR ) , where free-hand human sketches are used as queries to perform instance-level retrieval of images . This is an extremely challenging task because ( i ) visual comparisons not only need to be fine-grained but also executed cross-domain , ( ii ) free-hand ( finger ) sketches are highly abstract , making fine-grained matching harder , and most importantly ( iii ) annotated cross-domain sketch-photo datasets required for training are scarce , challenging many state-of-the-art machine learning techniques .
Current manifold ranking is mainly used in single-instance image retrieval without considering the prevailing semantic ambiguity problem . This paper introduces multi-instance technique and supervised information to image retrieval based on manifold ranking , and proposes a Multi-label Supervised Manifold Ranking algorithm ( MSMR ) for multi-instance image retrieval .
Combining diversity-based active learning with discriminant analysis in image retrieval A semi-supervised research approach to web-Image Re-ranking: Semantic image search engine Class-Specific Color Camera Calibration with Application to Object Recognition Inverted Index Compression for Scalable Image Matching Improved Content based Texture Image Classification using Cascade RBF GhostVLAD for set-based face recognition Order-Embeddings of Images and Language Generating Images from Captions with Attention A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database Buoy indexing of metric feature spaces for fast approximate image queries Uncertainty Analysis for Spatial Image Extractions in the context of Ontology and Fuzzy C-Means Algorithm Unsupervised clustering for nontextual web document classification 12 – Active Indexing Texture Image Retrival Based on Wavelet Decomposition and Gray Level Co-occurrence Matrix Object-Based Aggregation of Deep Features for Image Retrieval Improving Archaeologists’ Online Archive Experiences Through User-Centred Design Synobins: An Intermediate Level towards Annotation and Semantic Retrieval Image Classification Using Multiresolution Color , Texture And Shape Features Hybrid Features Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Quadruplet Networks for Sketch-Based Image Retrieval Sketch based Image Retrieval using Learned KeyShapes (LKS) Supporting sketch-based retrieval from a library of reusable behaviours Why does the sketch look so vivid? ADAM: a system for jointly providing ir and database queries in large-scale multimedia retrieval Sketch Image Retrieval System Using Canny Edge Detection for the Multiple Resolution Images Advanced Sketch Based Image Retrieval System Using Object Boundary Selection Algorithm A sketch image retrieval system using directional projection: DPSIR Fine-Grained Instance-Level Sketch-Based Video Retrieval Retrieval of 2D vector images by matching Weighted Feature Points Comparative Analysis on Image Retrieval Technique using Machine Learning A practical guide to CNNs and Fisher Vectors for image instance retrieval A Multi-Microprocessor Architecture for Associative Processing of Image Data ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval ACTNET: end-to-end learning of feature activations and aggregation for effective instance image retrieval Metric Embedding Learning on Multi-Directional Projections MIRROR: an interactive content based image retrieval system Delving into deep representations for remote sensing image retrieval Unsupervised Part Learning for Visual Recognition Sketch Me That Shoe Multi-label Supervised Manifold Ranking for Multi-instance Image Retrieval
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ICITA (1) WACV/MOTION DCC ACCV ICLR ICLR Eurographics Multimedia Workshop MMM (1) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICMR BMVC ICIMCS SIGIR ICASSP (2) ICIP ISCAS (2) CVPR CVPR RSKT
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