We propose to uncover those underlying patterns by detecting the communities in a network structure . Third , we leverage the visual and semantic correspondence and the co-occurrence patterns to improve the accuracy and efficiency for image retrieval . We perform experiments on two popular datasets that confirm the effectiveness of our approach . The content of an image can not be precisely described ; spatial entities ( objects ) and relationships ( image features ) in images do not in themselves carry any semantic meanings ; image-based information could be queried by pictures . The theory of symbolic projection , its applications , and systems incorporating advanced spatial reasoning and image information retrieval , may prove valuable when the design of the next generation of active image information systems is considered . The technology to access these images has also accelerated phenomenally and at present surpasses our understanding of how users interact with visual information . This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research . The approach is broad and interdisciplinary and focuses on three aspects of image research ( IR ) : text-based retrieval , content-based retrieval , and user interactions with image information retrieval systems . In inter-patch compression , one strives to transmit each patch with as few bits as possible by simply modifying a previously transmitted patch . This enables server-based mobile augmented reality where a continuous stream of salient information , sufficient for the image-based retrieval , can be sent over a wireless link at the smallest possible bit-rate . Experimental results show that our technique achieves a similar image matching performance at 1/10 of the bit-rate when compared to detecting key points independently frame-by-frame . The improvement of the accuracy of image query retrieval used image classification technique . Image classification is well known technique of supervised learning . Due to the dynamic nature of feature content of image of image user query are frequently changed and result of retrieval image are suffered . 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 . We demonstrate the usefulness of the proposed color invariants in image retrieval by example systems . We show that an image retrieval query should include the type of invariance expected in the result . We demonstrate such queries by using the " ImageSurf " retrieval system . The top-5 error rate for the entire two-level architecture needs to be computed in conjunction with the error rates of root & all the leaf models . The realization of this two level visual recognition architecture will greatly enhance the accuracy of the large scale object recognition scenarios demanded by the use cases as diverse as drone vision , augmented reality , retail , image search & retrieval , robotic navigation , targeted advertisements etc . Second , the full - image view is methodologically consistent with the traditional methods where the encoded SIFT vectors of the entire image are pooled together . Third , in other applications such as image retrieval , an image representation , rather than a classification score , is required for similarity ranking . A full - image representation can be preferred . This shows that the state of excitation or inhibition of neurons already contains the majority of discriminative information . Binary code is economic for storage and fast for image search . We believe this would be an interesting direction of future work . section : Introduction Person re - identification ( re - ID ) is usually viewed as an image retrieval problem , which matches pedestrians from different cameras . Given a person - of - interest ( query ) , person re - ID determines whether the person has been observed by another camera . Cluster-(K)CCA is shown to be computationally efficient , the complexity being similar to standard (K)CCA . By means of experimental evaluation on benchmark datasets , cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks . Content is increasingly available in multiple modalities ( such as images , text , and video ) , each of which provides a different representation of some entity . The cross-modal retrieval problem is : given the representation of an entity in one modality , find its best representation in all other modalities . We propose a novel approach to this problem based on pairwise classification . Abstract Hashing has been widely exploited in the cross-modal retrieval applications in recent years for its low storage cost and high retrieval efficiency . However , most existing cross-modal hashing methods either fail to capture the discriminative semantics of multi-modal data or suffer from the relatively high training cost . Instead of extracting the melody explicitly , another strategy is to perform the cross-modal comparison directly on the basis of melody-enhanced salience representations . As the main contribution of this paper , we evaluate several conceptually different salience representations for our cross-modal retrieval scenario . Our extensive experimental results , which have been made available on a website , comprise more than 2000 musical themes and 100 hours of audio recordings . The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals , which can be flexibly integrated with different types of loss functions . We conduct extensive experiments for both single-modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets . Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods . Thus , directly applying existing multiple-instance learning methods into deep hashing framework still leads to suboptimal hash codes for retrieval . In this paper , we pose hashing learning for multi-label image retrieval as a problem of multiple-instance ranking learning . To solve this problem , we present an end-to-end deep hashing framework , referred to as Deep Multiple-Instance Ranking based Hashing ( DMIRH ) . Deep supervised hashing methods for multi-label image retrieval have achieved great success nowadays . However , these methods only take the similarity between the database images and the query images into account , but they ignore the uniqueness of the database images when deciding on their rankings . By introducing the HSIC-based regularization term , the correlation information among multiple labels can be not only leveraged but also the consistency between the modality similarity from each modality is well preserved . Besides , based on the semantic-consistency projection , the semantic gap between the low-level feature space of each modality and the shared high-level semantic space can be balanced by a mid-level consistent one , where multi-label cross-modal retrieval can be performed effectively and efficiently . To solve the optimization problem , an effective iterative algorithm is designed , along with its convergence analysis theoretically and experimentally . One of the most challenging tasks in large-scale multi-label image retrieval is to map images into binary codes while preserving multilevel semantic similarity . Recently , several deep supervised hashing methods have been proposed to learn hash functions that preserve multilevel semantic similarity with deep convolutional neural networks . The picture information is the most important one of multimedia . It is necessary to retrieve or to process complex image information in the image database system , but the function of traditional label based image retrieval is quite limited , so the research for the technology of content based image retrieval is emphasized . This article will discuss the develompment of content based image database system and the relevant image retrieval and query technologies used . In the area of multi-label image categorization , there are two important issues : label classification and label ranking . The former refers to whether a label is relevant or not , and the latter refers to what extent a label is relevant to an image . These sparse reconstructions are used to estimate the likelihood of a scene to contain a land-cover class label . Applying this method separately for each land-cover class , one achieves retrieval in the framework of multi-label remote sensing image retrieval . Experimental results obtained on an archive of hyperspectral images show the effectiveness of the proposed system . The whole system has an end-to-end differentiable architecture , equipped with an adversarial mechanism to further improve the quality of the final intention representation . Experimental results on four multi-label image datasets demonstrate that our method significantly improves the state-of-the-art in performing complex multi-label retrieval tasks with multiple query images . However , existing zeor-shot hashing methods focus on the retrieval of single-label images , and can not handle multi-label images . In this paper , for the first time , a novel transductive zero-shot hashing method is proposed for multi-label unseen image retrieval . In order to predict the labels of the unseen/target data , a visual-semantic bridge is built via instance-concept coherence ranking on the seen/source data . Moreover , we utilize the pairwise supervised information to construct the fined semantic neighbourhood relationship between embeddings features . Finally , we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture . Experimental results on two largescale datasets demonstrate that the proposed method can outperform the state-of-the-art hashing methods . mage retrieval based on deep hashing methods has attracted more and more attentions from both academic and industry , due to the out-standing performance of deep neural network in various tasks of computer vision . However , most of the hashing methods are designed to learn simple similarity only for single-label image retrieval , thus can not work well for the multi-label cases . In this paper , we proposed a framework named Deep Multi-Similarity Hashing ( DMSH ) method to learn semantic binary representations for multi-label image retrieval task . Although PF - DLDL significantly outperforms IF - DLDL in mAP , PF - DLDL has higher computational cost than IF - DLDL on both training and testing stages . Since IF - DLDL does not need region proposals or bounding box information , it may be effectively and efficiently implemented for practical multi - label application such as multi - label image retrieval . It is also possible that by adopting new techniques ( such as the region proposal method using gated unit in , which has higher accuracy that ours on VOC tasks ) , the accuracy of our DLDL methods can be further improved . [ 3 ] K. Athanasakos , V. Stathopoulos , J. Jose , “ A framework for evaluating automatic image annotation algorithms ” , Lecture Notes in Computer Science , 5993:217 - 228 , 2010 . [ 4 ] R. Zhang , Z. Zhang , M. Li , H. J. Zhang , “ A probabilistic semantic model for image annotation and multimodal image retrieval ” , Multimedia Systems , pages 12(1):27 - 33 , 2006 . [ 5 ] Z. Theodosiou , N. Tsapatsoulis , “ Semantic Gap Between People : An Experimental Investigation based on Image Annotation ” , Proc . of the 7th International Workshop on Semantic Media Adaptation and Personalization , Luxembourg , 73 - 77 , 2012 . We improved results by examination of different level of integrated approach for retrieved text data and low level features . We tested multi-modality image retrieval in ImageCLEF 2012 medical retrieval task and obtained the best rank in visual retrieval due to our experiments . The results clearly show that proper combination of different modalities improve the overall retrieval performance . We improved results by examination of different weighting models for retrieved text data and low level features . We tested multi-modality image retrieval in ImageCLEF 2011 medical retrieval task and obtained the best seven ranks in mixed retrieval , which includes textual and visual modalities . The results clearly show that proper fusion of different modalities improve the overall retrieval performance . This paper describes a interactive multimodal image retrieval on mobile ( smartphone ) devices . For full advantage of the user can easily search few natural multimodal interactions with his/her mobile devices.the multimodal input and natural user interactions of mobile devices , the system , the Joint search with ImaGe , Speech , And Word Plus ( JIGSAW ) . Aiming at improving the research community ’s comprehension of this topic , we analyze and systematically categorize existing multi-modal search techniques , identify their strengths , and describe selected representatives . In the second part of the paper , we focus on the specific problem of large-scale multi-modal image retrieval on the web . We analyze the requirements of such task , implement several applicable fusion methods , and experimentally evaluate their performance in terms of both efficiency and effectiveness . In addition , our strategy can be usefully used in single-modal retrieval task . Extensive experiments show that our FSPDH approach yields state-of-the-art results in both multi-modal and single-modal image retrieval tasks on our image datasets . In order to attack large-scale multimedia applications , we propose Online Multi-modal Distance Learning ( OMDL ) algorithms , which are significantly more efficient and scalable than the state-of-the-art techniques . We conducted an extensive set of experiments on multi-modal image retrieval applications , in which encouraging results validate the efficacy of the proposed technique . Common patterns and probable transitions dealt with querying , inspecting result images and saving them into the workspace or iterating queries of the same type . The results indicate a need to support multimodal image query formulation . We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes . Our results showed that our method enabled the retrieval of multi-modality images using spatial features . Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques : gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform ( SIFT ) and relational matrices representing the spatial arrangements of objects .
We propose to uncover those underlying patterns by detecting the communities in a network structure . Third , we leverage the visual and semantic correspondence and the co-occurrence patterns to improve the accuracy and efficiency for image retrieval . We perform experiments on two popular datasets that confirm the effectiveness of our approach .
The content of an image can not be precisely described ; spatial entities ( objects ) and relationships ( image features ) in images do not in themselves carry any semantic meanings ; image-based information could be queried by pictures . The theory of symbolic projection , its applications , and systems incorporating advanced spatial reasoning and image information retrieval , may prove valuable when the design of the next generation of active image information systems is considered .
The technology to access these images has also accelerated phenomenally and at present surpasses our understanding of how users interact with visual information . This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research . The approach is broad and interdisciplinary and focuses on three aspects of image research ( IR ) : text-based retrieval , content-based retrieval , and user interactions with image information retrieval systems .
In inter-patch compression , one strives to transmit each patch with as few bits as possible by simply modifying a previously transmitted patch . This enables server-based mobile augmented reality where a continuous stream of salient information , sufficient for the image-based retrieval , can be sent over a wireless link at the smallest possible bit-rate . Experimental results show that our technique achieves a similar image matching performance at 1/10 of the bit-rate when compared to detecting key points independently frame-by-frame .
The improvement of the accuracy of image query retrieval used image classification technique . Image classification is well known technique of supervised learning .
Due to the dynamic nature of feature content of image of image user query are frequently changed and result of retrieval image are suffered . 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 .
We demonstrate the usefulness of the proposed color invariants in image retrieval by example systems . We show that an image retrieval query should include the type of invariance expected in the result . We demonstrate such queries by using the " ImageSurf " retrieval system .
The top-5 error rate for the entire two-level architecture needs to be computed in conjunction with the error rates of root & all the leaf models . The realization of this two level visual recognition architecture will greatly enhance the accuracy of the large scale object recognition scenarios demanded by the use cases as diverse as drone vision , augmented reality , retail , image search & retrieval , robotic navigation , targeted advertisements etc .
Second , the full - image view is methodologically consistent with the traditional methods where the encoded SIFT vectors of the entire image are pooled together . Third , in other applications such as image retrieval , an image representation , rather than a classification score , is required for similarity ranking . A full - image representation can be preferred .
This shows that the state of excitation or inhibition of neurons already contains the majority of discriminative information . Binary code is economic for storage and fast for image search . We believe this would be an interesting direction of future work .
section : Introduction Person re - identification ( re - ID ) is usually viewed as an image retrieval problem , which matches pedestrians from different cameras . Given a person - of - interest ( query ) , person re - ID determines whether the person has been observed by another camera .
Cluster-(K)CCA is shown to be computationally efficient , the complexity being similar to standard (K)CCA . By means of experimental evaluation on benchmark datasets , cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks .
Content is increasingly available in multiple modalities ( such as images , text , and video ) , each of which provides a different representation of some entity . The cross-modal retrieval problem is : given the representation of an entity in one modality , find its best representation in all other modalities . We propose a novel approach to this problem based on pairwise classification .
Abstract Hashing has been widely exploited in the cross-modal retrieval applications in recent years for its low storage cost and high retrieval efficiency . However , most existing cross-modal hashing methods either fail to capture the discriminative semantics of multi-modal data or suffer from the relatively high training cost .
Instead of extracting the melody explicitly , another strategy is to perform the cross-modal comparison directly on the basis of melody-enhanced salience representations . As the main contribution of this paper , we evaluate several conceptually different salience representations for our cross-modal retrieval scenario . Our extensive experimental results , which have been made available on a website , comprise more than 2000 musical themes and 100 hours of audio recordings .
The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals , which can be flexibly integrated with different types of loss functions . We conduct extensive experiments for both single-modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets . Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods .
Thus , directly applying existing multiple-instance learning methods into deep hashing framework still leads to suboptimal hash codes for retrieval . In this paper , we pose hashing learning for multi-label image retrieval as a problem of multiple-instance ranking learning . To solve this problem , we present an end-to-end deep hashing framework , referred to as Deep Multiple-Instance Ranking based Hashing ( DMIRH ) .
Deep supervised hashing methods for multi-label image retrieval have achieved great success nowadays . However , these methods only take the similarity between the database images and the query images into account , but they ignore the uniqueness of the database images when deciding on their rankings .
By introducing the HSIC-based regularization term , the correlation information among multiple labels can be not only leveraged but also the consistency between the modality similarity from each modality is well preserved . Besides , based on the semantic-consistency projection , the semantic gap between the low-level feature space of each modality and the shared high-level semantic space can be balanced by a mid-level consistent one , where multi-label cross-modal retrieval can be performed effectively and efficiently . To solve the optimization problem , an effective iterative algorithm is designed , along with its convergence analysis theoretically and experimentally .
One of the most challenging tasks in large-scale multi-label image retrieval is to map images into binary codes while preserving multilevel semantic similarity . Recently , several deep supervised hashing methods have been proposed to learn hash functions that preserve multilevel semantic similarity with deep convolutional neural networks .
The picture information is the most important one of multimedia . It is necessary to retrieve or to process complex image information in the image database system , but the function of traditional label based image retrieval is quite limited , so the research for the technology of content based image retrieval is emphasized . This article will discuss the develompment of content based image database system and the relevant image retrieval and query technologies used .
In the area of multi-label image categorization , there are two important issues : label classification and label ranking . The former refers to whether a label is relevant or not , and the latter refers to what extent a label is relevant to an image .
These sparse reconstructions are used to estimate the likelihood of a scene to contain a land-cover class label . Applying this method separately for each land-cover class , one achieves retrieval in the framework of multi-label remote sensing image retrieval . Experimental results obtained on an archive of hyperspectral images show the effectiveness of the proposed system .
The whole system has an end-to-end differentiable architecture , equipped with an adversarial mechanism to further improve the quality of the final intention representation . Experimental results on four multi-label image datasets demonstrate that our method significantly improves the state-of-the-art in performing complex multi-label retrieval tasks with multiple query images .
However , existing zeor-shot hashing methods focus on the retrieval of single-label images , and can not handle multi-label images . In this paper , for the first time , a novel transductive zero-shot hashing method is proposed for multi-label unseen image retrieval . In order to predict the labels of the unseen/target data , a visual-semantic bridge is built via instance-concept coherence ranking on the seen/source data .
Moreover , we utilize the pairwise supervised information to construct the fined semantic neighbourhood relationship between embeddings features . Finally , we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture . Experimental results on two largescale datasets demonstrate that the proposed method can outperform the state-of-the-art hashing methods .
mage retrieval based on deep hashing methods has attracted more and more attentions from both academic and industry , due to the out-standing performance of deep neural network in various tasks of computer vision . However , most of the hashing methods are designed to learn simple similarity only for single-label image retrieval , thus can not work well for the multi-label cases . In this paper , we proposed a framework named Deep Multi-Similarity Hashing ( DMSH ) method to learn semantic binary representations for multi-label image retrieval task .
Although PF - DLDL significantly outperforms IF - DLDL in mAP , PF - DLDL has higher computational cost than IF - DLDL on both training and testing stages . Since IF - DLDL does not need region proposals or bounding box information , it may be effectively and efficiently implemented for practical multi - label application such as multi - label image retrieval . It is also possible that by adopting new techniques ( such as the region proposal method using gated unit in , which has higher accuracy that ours on VOC tasks ) , the accuracy of our DLDL methods can be further improved .
[ 3 ] K. Athanasakos , V. Stathopoulos , J. Jose , “ A framework for evaluating automatic image annotation algorithms ” , Lecture Notes in Computer Science , 5993:217 - 228 , 2010 . [ 4 ] R. Zhang , Z. Zhang , M. Li , H. J. Zhang , “ A probabilistic semantic model for image annotation and multimodal image retrieval ” , Multimedia Systems , pages 12(1):27 - 33 , 2006 . [ 5 ] Z. Theodosiou , N. Tsapatsoulis , “ Semantic Gap Between People : An Experimental Investigation based on Image Annotation ” , Proc . of the 7th International Workshop on Semantic Media Adaptation and Personalization , Luxembourg , 73 - 77 , 2012 .
We improved results by examination of different level of integrated approach for retrieved text data and low level features . We tested multi-modality image retrieval in ImageCLEF 2012 medical retrieval task and obtained the best rank in visual retrieval due to our experiments . The results clearly show that proper combination of different modalities improve the overall retrieval performance .
We improved results by examination of different weighting models for retrieved text data and low level features . We tested multi-modality image retrieval in ImageCLEF 2011 medical retrieval task and obtained the best seven ranks in mixed retrieval , which includes textual and visual modalities . The results clearly show that proper fusion of different modalities improve the overall retrieval performance .
This paper describes a interactive multimodal image retrieval on mobile ( smartphone ) devices . For full advantage of the user can easily search few natural multimodal interactions with his/her mobile devices.the multimodal input and natural user interactions of mobile devices , the system , the Joint search with ImaGe , Speech , And Word Plus ( JIGSAW ) .
Aiming at improving the research community ’s comprehension of this topic , we analyze and systematically categorize existing multi-modal search techniques , identify their strengths , and describe selected representatives . In the second part of the paper , we focus on the specific problem of large-scale multi-modal image retrieval on the web . We analyze the requirements of such task , implement several applicable fusion methods , and experimentally evaluate their performance in terms of both efficiency and effectiveness .
In addition , our strategy can be usefully used in single-modal retrieval task . Extensive experiments show that our FSPDH approach yields state-of-the-art results in both multi-modal and single-modal image retrieval tasks on our image datasets .
In order to attack large-scale multimedia applications , we propose Online Multi-modal Distance Learning ( OMDL ) algorithms , which are significantly more efficient and scalable than the state-of-the-art techniques . We conducted an extensive set of experiments on multi-modal image retrieval applications , in which encouraging results validate the efficacy of the proposed technique .
Common patterns and probable transitions dealt with querying , inspecting result images and saving them into the workspace or iterating queries of the same type . The results indicate a need to support multimodal image query formulation .
We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes . Our results showed that our method enabled the retrieval of multi-modality images using spatial features . Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques : gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform ( SIFT ) and relational matrices representing the spatial arrangements of objects .
Semantic-visual concept relatedness and co-occurrences for image retrieval A Survey of Image Information Systems and Future Directions Image Information Retrieval: An Overview of Current Research Interframe Coding of Canonical Patches for Mobile Augmented Reality Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey Improved Content based Texture Image Classification using Cascade RBF Image retrieval and segmentation based on color invariants HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10 K OBJECTS Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Deeply learned face representations are sparse, selective, and robust A Discriminatively Learned CNN Embedding for Person Reidentification Cluster Canonical Correlation Analysis Cross-Modal Retrieval: A Pairwise Classification Approach Efficient discrete latent semantic hashing for scalable cross-modal retrieval Evaluating Salience Representations for Cross-modal Retrieval of Western Classical Music Recordings Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals. Multiple-Instance ranking based deep hashing for multi-Label image retrieval Deep Uniqueness-Aware Hashing for Fine-Grained Multi-Label Image Retrieval Towards learning a semantic-consistent subspace for cross-modal retrieval Deep Supervised Hashing for Multi-Label and Large-Scale Image Retrieval The Image Database System of Content Based Query Calibrated Rank-SVM for multi-label image categorization A novel system for content based retrieval of multi-label remote sensing images Deep code operation network for multi-label image retrieval Transductive Zero-Shot Hashing for Multi-Label Image Retrieval Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval Deep Multi-Similarity Hashing for Multi-label Image Retrieval Deep Label Distribution Learning With Label Ambiguity Image Retrieval: Modelling Keywords via Low-level Features DEMIR at ImageCLEFMed 2012: Inter-modality and Intra-Modality Integrated Combination Retrieval DEMIR at ImageCLEFMed 2011: Evaluation of Fusion Techniques for Multimodal Content-based Medical Image Retrieval Mobile searching based on image,speech and text queries Fusion Strategies for Large-Scale Multi-modal Image Retrieval Fusing Semantic Prior Based Deep Hashing Method for Fuzzy Image Retrieval Online multi-modal distance learning for scalable multimedia retrieval Search strategies in multimodal image retrieval A graph-based approach for the retrieval of multi-modality medical images
a6b7d2b8deb69b4ba64949a6f8c13c0ca00a0a8e b1783322f42daef5f0cff039854615666c3fb864 bd1cc7c20b67ade9ffbe548c95bd2e31af66a1a3 6b8cc207a42a78ba00adb7c8ae6d38e8d40cf71d 0a0ab7edf94a148476cf8b96922269da715e018d da1e0e249d29554969ea7992a37b14124914e4a3 c181024cb382404152d354aabf928827fbc8ad7d 213a8b1eea4ef4440f7861eabb071538e1950d71 3c5653a8e607f436465ade84e3d6134df99cc728 81148f31686eb077ab15bfe70ec3f9d6c8508ec7 aefb7243028a269f8c87770364c674a37d6ef72e c02752779e5c124721e2fbf13f840228dab921e8 f5d1221fd696b6fafaadb7d3bde32085032e9bde bc5a8b6a44b714eeaf9a702c60ec057f4caa645a d9a0ccbef012d48813334be3de8319f8dd98e3fb 33bda213520d4c1b5e6cadd0954cb4956d9fa343 f41e68b280c05b89cc3c013ce680870b5f23e3e0 c8bc1ac9701985a75e45a8777867d6fb9ddd30d8 d708874df9a2334cd45ceab1ebafe9cf25b413a3 e74c4cdf6d5cc61aed6c563a42d4e8cdf8b30d34 426b15a4ec3a6b767166bb7f77b2aa71bd5389eb ed5a1abda1c9d6796e822b112dc97c9a27ccec51 3de6045298ccde076ce081b6e4aa1881ed9c4181 9aca8b77da409d551ae92bfcfa91f1f1d80fc06c b93613f10d8d556a3ca94d114c9440810204d7b6 e66b9268a6f0e72d4d3ce9e65a87de62b29a2a06 3c86fec6613069b0086274675cbc0f05a3f68bf0 9ce60d7073e21dfbc647dc9142d0de7140582eee b3a962b95ce8ab367e808efdb64a2acf9a0e2cb2 26d0261ccfd005a048aee6c8609334b20c0502a1 cb1478c49b430d3ad27feb2decef428cdae37f12 e4085a3045ef2bc4e6e1a34f340f74bd06d406de ff51b726d389bbba2ca8c426a1077005eb93d793
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science","Medicine"]
2012 1996 2000 2012 2013 2013 2000 2015 2015 2015 2018 2014 2015 2019 2019 2020 2020 2018 2019 2017 2001 2008 2017 2020 2019 2019 2017 2017 2015 2012 2011 2016 2017 2018 2013 2008 2014
ICIP ISM CVPR IEEE Transactions on Pattern Analysis and Machine Intelligence 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACM Trans. Multim. Comput. Commun. Appl. AISTATS SDM ICASSP ICASSP ICMR IJCNN IGARSS CIKM IEEE Transactions on Image Processing CLEF (Notebook Papers/Labs/Workshop) PRICAI (1) WSDM IIiX
https://api.semanticscholar.org/a6b7d2b8deb69b4ba64949a6f8c13c0ca00a0a8e https://api.semanticscholar.org/b1783322f42daef5f0cff039854615666c3fb864 https://api.semanticscholar.org/bd1cc7c20b67ade9ffbe548c95bd2e31af66a1a3 https://api.semanticscholar.org/6b8cc207a42a78ba00adb7c8ae6d38e8d40cf71d https://api.semanticscholar.org/0a0ab7edf94a148476cf8b96922269da715e018d https://api.semanticscholar.org/da1e0e249d29554969ea7992a37b14124914e4a3 https://api.semanticscholar.org/c181024cb382404152d354aabf928827fbc8ad7d https://api.semanticscholar.org/213a8b1eea4ef4440f7861eabb071538e1950d71 https://www.semanticscholar.org/paper/cbb19236820a96038d000dc629225d36e0b6294a https://www.semanticscholar.org/paper/e70d75454de81b05fed9b63a405bcd1229f20229 https://www.semanticscholar.org/paper/193089d56758ab88391d846edd08d359b1f9a863 https://api.semanticscholar.org/3c5653a8e607f436465ade84e3d6134df99cc728 https://api.semanticscholar.org/81148f31686eb077ab15bfe70ec3f9d6c8508ec7 https://api.semanticscholar.org/aefb7243028a269f8c87770364c674a37d6ef72e https://api.semanticscholar.org/c02752779e5c124721e2fbf13f840228dab921e8 https://api.semanticscholar.org/f5d1221fd696b6fafaadb7d3bde32085032e9bde https://api.semanticscholar.org/bc5a8b6a44b714eeaf9a702c60ec057f4caa645a https://api.semanticscholar.org/d9a0ccbef012d48813334be3de8319f8dd98e3fb https://api.semanticscholar.org/33bda213520d4c1b5e6cadd0954cb4956d9fa343 https://api.semanticscholar.org/f41e68b280c05b89cc3c013ce680870b5f23e3e0 https://api.semanticscholar.org/c8bc1ac9701985a75e45a8777867d6fb9ddd30d8 https://api.semanticscholar.org/d708874df9a2334cd45ceab1ebafe9cf25b413a3 https://api.semanticscholar.org/e74c4cdf6d5cc61aed6c563a42d4e8cdf8b30d34 https://api.semanticscholar.org/426b15a4ec3a6b767166bb7f77b2aa71bd5389eb https://api.semanticscholar.org/ed5a1abda1c9d6796e822b112dc97c9a27ccec51 https://api.semanticscholar.org/3de6045298ccde076ce081b6e4aa1881ed9c4181 https://api.semanticscholar.org/9aca8b77da409d551ae92bfcfa91f1f1d80fc06c https://www.semanticscholar.org/paper/e9b5098f6083585c15f9a6315f457db98086bf82 https://api.semanticscholar.org/b93613f10d8d556a3ca94d114c9440810204d7b6 https://api.semanticscholar.org/e66b9268a6f0e72d4d3ce9e65a87de62b29a2a06 https://api.semanticscholar.org/3c86fec6613069b0086274675cbc0f05a3f68bf0 https://api.semanticscholar.org/9ce60d7073e21dfbc647dc9142d0de7140582eee https://api.semanticscholar.org/b3a962b95ce8ab367e808efdb64a2acf9a0e2cb2 https://api.semanticscholar.org/26d0261ccfd005a048aee6c8609334b20c0502a1 https://api.semanticscholar.org/cb1478c49b430d3ad27feb2decef428cdae37f12 https://api.semanticscholar.org/e4085a3045ef2bc4e6e1a34f340f74bd06d406de https://api.semanticscholar.org/ff51b726d389bbba2ca8c426a1077005eb93d793