Feature-Based Sparse Representation for Image Similarity Assessment
Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an inf...
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| Veröffentlicht in: | IEEE transactions on multimedia 2011-10, Vol.13 (5), p.1019-1030 |
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| container_title | IEEE transactions on multimedia |
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| creator | KANG, Li-Wei HSU, Chao-Yung CHEN, Hung-Wei LU, Chun-Shien LIN, Chih-Yang PEI, Soo-Chang |
| description | Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets. |
| doi_str_mv | 10.1109/TMM.2011.2159197 |
| format | Article |
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The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets.</description><identifier>ISSN: 1520-9210</identifier><identifier>EISSN: 1941-0077</identifier><identifier>DOI: 10.1109/TMM.2011.2159197</identifier><identifier>CODEN: ITMUF8</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Assessments ; Coiling ; Computational complexity ; Computer science; control theory; systems ; Data mining ; Detection, estimation, filtering, equalization, prediction ; Dictionaries ; Encoding ; Exact sciences and technology ; Feature detection ; Feature extraction ; Image coding ; image copy detection ; image recognition ; Image reconstruction ; image retrieval ; image similarity assessment ; Information, signal and communications theory ; Learning ; Multimedia ; Pattern recognition. Digital image processing. Computational geometry ; Representations ; Signal and communications theory ; Signal, noise ; Similarity ; sparse representation ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transactions on multimedia, 2011-10, Vol.13 (5), p.1019-1030</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Assessments</subject><subject>Coiling</subject><subject>Computational complexity</subject><subject>Computer science; control theory; systems</subject><subject>Data mining</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Dictionaries</subject><subject>Encoding</subject><subject>Exact sciences and technology</subject><subject>Feature detection</subject><subject>Feature extraction</subject><subject>Image coding</subject><subject>image copy detection</subject><subject>image recognition</subject><subject>Image reconstruction</subject><subject>image retrieval</subject><subject>image similarity assessment</subject><subject>Information, signal and communications theory</subject><subject>Learning</subject><subject>Multimedia</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Representations</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Similarity</subject><subject>sparse representation</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1520-9210</issn><issn>1941-0077</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMtLxEAMxoso-LwLXoogeOmaeXVmjrr4AhfB3XuJbSqVvpx0D_vfO8suHjwlkN_3JfmS5FLATAjwd6vFYiZBiJkUxgtvD5IT4bXIAKw9jL2RkHkp4Dg5Zf4GENqAPUnmT4TTOlD2gExVuhwxMKUfNAZi6iecmqFP6yGkrx1-UbpsuqbF0Eyb9J6ZmLsInSdHNbZMF_t6lqyeHlfzl-zt_fl1fv-WlcqbKfv0canMkeyn8qISVVkZU-paKWWcBqvJY1W6yjuHqNFUVvr4mgOvwKFUZ8ntznYMw8-aeCq6hktqW-xpWHMhQAkVbZSP6PU_9HtYhz4eVzgPyuVGQYRgB5VhYA5UF2NoOgyb6FRsMy1ipsU202KfaZTc7H2RS2zrgH3Z8J9OapNb7fPIXe24hoj-xsZZCdqrXyJEfYo</recordid><startdate>20111001</startdate><enddate>20111001</enddate><creator>KANG, Li-Wei</creator><creator>HSU, Chao-Yung</creator><creator>CHEN, Hung-Wei</creator><creator>LU, Chun-Shien</creator><creator>LIN, Chih-Yang</creator><creator>PEI, Soo-Chang</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Computational geometry</topic><topic>Representations</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Similarity</topic><topic>sparse representation</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KANG, Li-Wei</creatorcontrib><creatorcontrib>HSU, Chao-Yung</creatorcontrib><creatorcontrib>CHEN, Hung-Wei</creatorcontrib><creatorcontrib>LU, Chun-Shien</creatorcontrib><creatorcontrib>LIN, Chih-Yang</creatorcontrib><creatorcontrib>PEI, Soo-Chang</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>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>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on multimedia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>KANG, Li-Wei</au><au>HSU, Chao-Yung</au><au>CHEN, Hung-Wei</au><au>LU, Chun-Shien</au><au>LIN, Chih-Yang</au><au>PEI, Soo-Chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feature-Based Sparse Representation for Image Similarity Assessment</atitle><jtitle>IEEE transactions on multimedia</jtitle><stitle>TMM</stitle><date>2011-10-01</date><risdate>2011</risdate><volume>13</volume><issue>5</issue><spage>1019</spage><epage>1030</epage><pages>1019-1030</pages><issn>1520-9210</issn><eissn>1941-0077</eissn><coden>ITMUF8</coden><abstract>Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. 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| subjects | Applied sciences Artificial intelligence Assessments Coiling Computational complexity Computer science control theory systems Data mining Detection, estimation, filtering, equalization, prediction Dictionaries Encoding Exact sciences and technology Feature detection Feature extraction Image coding image copy detection image recognition Image reconstruction image retrieval image similarity assessment Information, signal and communications theory Learning Multimedia Pattern recognition. Digital image processing. Computational geometry Representations Signal and communications theory Signal, noise Similarity sparse representation Studies Telecommunications and information theory |
| title | Feature-Based Sparse Representation for Image Similarity Assessment |
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