{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:24:34Z","timestamp":1750307074602,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2012,11,1]],"date-time":"2012-11-01T00:00:00Z","timestamp":1351728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["60973115, 60973117, 61173160, 61173162, and 61173165"],"award-info":[{"award-number":["60973115, 60973117, 61173160, 61173162, and 61173165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:p>In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features.<\/jats:p>\n          <jats:p>In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.<\/jats:p>","DOI":"10.1145\/2379790.2379796","type":"journal-article","created":{"date-parts":[[2012,12,11]],"date-time":"2012-12-11T13:13:42Z","timestamp":1355231622000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Object-based image retrieval with kernel on adjacency matrix and local combined features"],"prefix":"10.1145","volume":"8","author":[{"given":"Heng","family":"Qi","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanming","family":"Shen","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Qu","sequence":"additional","affiliation":[{"name":"Dalian Maritime University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,11,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1023800"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.788646"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000022288.19776.77"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1282280.1282317"},{"volume-title":"Proceedings of IEEE International Conference on Image Processing. 177--180","author":"Gosselin P. 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Department of Computer Science National Taiwan University."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1631272.1631284"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.68"},{"volume-title":"Proceedings of the 19th International Conference on Pattern Recognition. 1--4.","author":"Lebrun J.","key":"e_1_2_1_16_1"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.223"},{"volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1--8.","author":"Perronnin F.","key":"e_1_2_1_18_1"},{"volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1--8.","author":"Philbin J.","key":"e_1_2_1_19_1"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.683777"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Shawe-Taylor J. and Cristianini N. 2004. Kernel Methods for Pattern Analysis. Cambridge University Press.   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John Wiley & Sons New York."},{"volume":"2","volume-title":"Proceedings of the 9th IEEE International Conference on Computer Vision.","author":"Wallraven C.","key":"e_1_2_1_24_1"},{"volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 3360--3367","author":"Wang J.","key":"e_1_2_1_25_1"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.955109"},{"volume":"2","volume-title":"Proceedings of International Conference on Image Processing.","author":"Zhang L.","key":"e_1_2_1_27_1"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1404880.1404887"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2379790.2379796","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2379790.2379796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:33:57Z","timestamp":1750239237000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2379790.2379796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,11]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,11]]}},"alternative-id":["10.1145\/2379790.2379796"],"URL":"https:\/\/doi.org\/10.1145\/2379790.2379796","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2012,11]]},"assertion":[{"value":"2011-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2011-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-11-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}