Learning Image Similarity from Flickr Groups Using Fast Kernel Machines
Measuring image similarity is a central topic in computer vision. In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taki...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2012-11, Vol.34 (11), p.2177-2188 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Gang Wang Hoiem, D. Forsyth, D. |
| description | Measuring image similarity is a central topic in computer vision. In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. This method can produce a kernel classifier that is more accurate than a linear classifier on tens of thousands of examples in minutes. |
| doi_str_mv | 10.1109/TPAMI.2012.29 |
| format | Article |
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In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. 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Digital image processing. Computational geometry ; Reproducibility of Results ; Sensitivity and Specificity ; Similarity ; stochastic gradient descent ; Studies ; Subtraction Technique ; Support vector machines ; Training ; Visualization</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2012-11, Vol.34 (11), p.2177-2188</ispartof><rights>2014 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. 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Computational geometry</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Similarity</subject><subject>stochastic gradient descent</subject><subject>Studies</subject><subject>Subtraction Technique</subject><subject>Support vector machines</subject><subject>Training</subject><subject>Visualization</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0c9r2zAUB3AxVpY023GnwTCUwi7OpCdZso6hNFloSgtLzkaWnzNl_pFK8aH_fe0my6CXnnTQ5z2k75eQr4xOGaP65_pxdr-cAmUwBf2BjJnmOuYJ1x_JmDIJcZpCOiKXIewoZSKh_BMZAWitGKgxWazQ-MY122hZmy1Gv13tKuPd4TkqfVtH88rZvz5a-Lbbh2gTBjk34RDdoW-wiu6N_eMaDJ_JRWmqgF9O54Rs5rfrm1_x6mGxvJmtYiuoOMTGQA4UrFKFYGjzxApjRFFI4IXImSpVqXMjqNYpSixySHjZT2gQUEiqgE_Ij-PevW-fOgyHrHbBYlWZBtsuZEwqxhMlkvR9SqUGqlLJe3r1hu7azjf9R14VZ1L2uU5IfFTWtyF4LLO9d7Xxzz3KhjKy1zKyoYwMBv_9tLXLayzO-l_6Pbg-AROsqUpvGuvCfyeVZgyG5307OoeI52vJOO-T4S9xkpgX</recordid><startdate>20121101</startdate><enddate>20121101</enddate><creator>Gang Wang</creator><creator>Hoiem, D.</creator><creator>Forsyth, D.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Computational geometry</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Similarity</topic><topic>stochastic gradient descent</topic><topic>Studies</topic><topic>Subtraction Technique</topic><topic>Support vector machines</topic><topic>Training</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gang Wang</creatorcontrib><creatorcontrib>Hoiem, D.</creatorcontrib><creatorcontrib>Forsyth, D.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gang Wang</au><au>Hoiem, D.</au><au>Forsyth, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Learning Image Similarity from Flickr Groups Using Fast Kernel Machines</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2012-11-01</date><risdate>2012</risdate><volume>34</volume><issue>11</issue><spage>2177</spage><epage>2188</epage><pages>2177-2188</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>Measuring image similarity is a central topic in computer vision. In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2012-11, Vol.34 (11), p.2177-2188 |
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| subjects | Algorithms Applied sciences Artificial Intelligence Classifiers Computer science control theory systems Euclidean distance Exact sciences and technology Feature extraction Histograms image classification Image Enhancement - methods Image Interpretation, Computer-Assisted - methods image organization Image similarity Intersections Kernel kernel machines Kernels Learning and adaptive systems Mathematical analysis online learning Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Sensitivity and Specificity Similarity stochastic gradient descent Studies Subtraction Technique Support vector machines Training Visualization |
| title | Learning Image Similarity from Flickr Groups Using Fast Kernel Machines |
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