Content-based image retrieval using combination features
Image retrieval based on single feature is already quite mature, how to use these single features effectively is the key issue in improving the effect of the image retrieval. We often use the combination features to solve the problem. In this paper, we use the dynamic weight to combination these sin...
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| creator | Du Yuncheng Gao Hui Wang Hongwei Shi Shuicai |
| description | Image retrieval based on single feature is already quite mature, how to use these single features effectively is the key issue in improving the effect of the image retrieval. We often use the combination features to solve the problem. In this paper, we use the dynamic weight to combination these single features. Experimental results show that the algorithm using dynamic weight can accurately and efficiently retrieve the target image compared with using the method of using fixed weight. The method using the dynamic weight effectively improves the image retrieval accuracy. |
| doi_str_mv | 10.1109/ICAIE.2010.5641032 |
| format | Conference Proceeding |
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We often use the combination features to solve the problem. In this paper, we use the dynamic weight to combination these single features. Experimental results show that the algorithm using dynamic weight can accurately and efficiently retrieve the target image compared with using the method of using fixed weight. The method using the dynamic weight effectively improves the image retrieval accuracy.</description><identifier>ISBN: 9781424469352</identifier><identifier>ISBN: 142446935X</identifier><identifier>EISBN: 1424469368</identifier><identifier>EISBN: 9781424469345</identifier><identifier>EISBN: 1424469341</identifier><identifier>EISBN: 9781424469369</identifier><identifier>DOI: 10.1109/ICAIE.2010.5641032</identifier><language>eng</language><publisher>IEEE</publisher><subject>dynamic weight ; Education ; fixed weight ; Image edge detection ; Image segmentation ; Multi-features image retrieval ; Shape</subject><ispartof>2010 International Conference on Artificial Intelligence and Education (ICAIE), 2010, p.659-662</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5641032$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5641032$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Du Yuncheng</creatorcontrib><creatorcontrib>Gao Hui</creatorcontrib><creatorcontrib>Wang Hongwei</creatorcontrib><creatorcontrib>Shi Shuicai</creatorcontrib><title>Content-based image retrieval using combination features</title><title>2010 International Conference on Artificial Intelligence and Education (ICAIE)</title><addtitle>ICAIE</addtitle><description>Image retrieval based on single feature is already quite mature, how to use these single features effectively is the key issue in improving the effect of the image retrieval. 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The method using the dynamic weight effectively improves the image retrieval accuracy.</description><subject>dynamic weight</subject><subject>Education</subject><subject>fixed weight</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Multi-features image retrieval</subject><subject>Shape</subject><isbn>9781424469352</isbn><isbn>142446935X</isbn><isbn>1424469368</isbn><isbn>9781424469345</isbn><isbn>1424469341</isbn><isbn>9781424469369</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j8tKAzEYhSNSUOu8gG7yAlNz-ZOZLGWoWii46b7k8k9JaTOSpIJv74D1bA7f5vAdQp44W3HOzMtmeN2sV4LNrDRwJsUNeeAgALSRur8ljen6f1bijjSlHNkcUAKkuCf9MKWKqbbOFgw0nu0BacaaI37bE72UmA7UT2cXk61xSnREWy8ZyyNZjPZUsLn2kuze1rvho91-vs9W2zYaVtsOtQroelTGWa_MKDoGlhsOqLTTQYPQHROj9dKh5hpUULOpgWCC98HIJXn-m42IuP_Ks2D-2V-_yl8-ukfU</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Du Yuncheng</creator><creator>Gao Hui</creator><creator>Wang Hongwei</creator><creator>Shi Shuicai</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Content-based image retrieval using combination features</title><author>Du Yuncheng ; Gao Hui ; Wang Hongwei ; Shi Shuicai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-7e65deb8e59bac59f2704a1914e56b6d6426702fac3be61645d569394d9dccd93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>dynamic weight</topic><topic>Education</topic><topic>fixed weight</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Multi-features image retrieval</topic><topic>Shape</topic><toplevel>online_resources</toplevel><creatorcontrib>Du Yuncheng</creatorcontrib><creatorcontrib>Gao Hui</creatorcontrib><creatorcontrib>Wang Hongwei</creatorcontrib><creatorcontrib>Shi Shuicai</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Explore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Du Yuncheng</au><au>Gao Hui</au><au>Wang Hongwei</au><au>Shi Shuicai</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Content-based image retrieval using combination features</atitle><btitle>2010 International Conference on Artificial Intelligence and Education (ICAIE)</btitle><stitle>ICAIE</stitle><date>2010-10</date><risdate>2010</risdate><spage>659</spage><epage>662</epage><pages>659-662</pages><isbn>9781424469352</isbn><isbn>142446935X</isbn><eisbn>1424469368</eisbn><eisbn>9781424469345</eisbn><eisbn>1424469341</eisbn><eisbn>9781424469369</eisbn><abstract>Image retrieval based on single feature is already quite mature, how to use these single features effectively is the key issue in improving the effect of the image retrieval. We often use the combination features to solve the problem. In this paper, we use the dynamic weight to combination these single features. Experimental results show that the algorithm using dynamic weight can accurately and efficiently retrieve the target image compared with using the method of using fixed weight. The method using the dynamic weight effectively improves the image retrieval accuracy.</abstract><pub>IEEE</pub><doi>10.1109/ICAIE.2010.5641032</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2010 International Conference on Artificial Intelligence and Education (ICAIE), 2010, p.659-662 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | dynamic weight Education fixed weight Image edge detection Image segmentation Multi-features image retrieval Shape |
| title | Content-based image retrieval using combination features |
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