Directional local extrema patterns: a new descriptor for content based image retrieval
In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed...
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| Veröffentlicht in: | International journal of multimedia information retrieval 2012-10, Vol.1 (3), p.191-203 |
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| container_title | International journal of multimedia information retrieval |
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| creator | Murala, Subrahmanyam Maheshwari, R. P. Balasubramanian, R. |
| description | In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0
, 45
, 90
, and 135
directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases. |
| doi_str_mv | 10.1007/s13735-012-0008-2 |
| format | Article |
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, 45
, 90
, and 135
directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.</description><identifier>ISSN: 2192-6611</identifier><identifier>EISSN: 2192-662X</identifier><identifier>DOI: 10.1007/s13735-012-0008-2</identifier><language>eng</language><publisher>London: Springer-Verlag</publisher><subject>Algorithms ; Classification ; Computer Science ; Data Mining and Knowledge Discovery ; Database Management ; Image Processing and Computer Vision ; Image retrieval ; Image segmentation ; Information Storage and Retrieval ; Information Systems Applications (incl.Internet) ; Multimedia Information Systems ; Regular Paper ; Retrieval ; Wavelet transforms</subject><ispartof>International journal of multimedia information retrieval, 2012-10, Vol.1 (3), p.191-203</ispartof><rights>Springer-Verlag London Limited 2012</rights><rights>Springer-Verlag London Limited 2012.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-dede15c22254ad45512265846f656341c56ed03897bfe1d022a96fe6a4506f883</citedby><cites>FETCH-LOGICAL-c359t-dede15c22254ad45512265846f656341c56ed03897bfe1d022a96fe6a4506f883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13735-012-0008-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920224959?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Murala, Subrahmanyam</creatorcontrib><creatorcontrib>Maheshwari, R. P.</creatorcontrib><creatorcontrib>Balasubramanian, R.</creatorcontrib><title>Directional local extrema patterns: a new descriptor for content based image retrieval</title><title>International journal of multimedia information retrieval</title><addtitle>Int J Multimed Info Retr</addtitle><description>In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0
, 45
, 90
, and 135
directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. 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, 45
, 90
, and 135
directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.</abstract><cop>London</cop><pub>Springer-Verlag</pub><doi>10.1007/s13735-012-0008-2</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Classification Computer Science Data Mining and Knowledge Discovery Database Management Image Processing and Computer Vision Image retrieval Image segmentation Information Storage and Retrieval Information Systems Applications (incl.Internet) Multimedia Information Systems Regular Paper Retrieval Wavelet transforms |
| title | Directional local extrema patterns: a new descriptor for content based image retrieval |
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