Loading…

Image retrieval based on feature weighting and relevance feedback

We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's...

Full description

Saved in:

Bibliographic Details
Main Authors:	Kherfi, M.L., Ziou, D.
Format:	Conference Proceeding
Language:	English
Subjects:	Applied sciences Artificial intelligence Bayesian methods Computer science control theory systems Content based retrieval Exact sciences and technology Feedback Image databases Image retrieval Information retrieval Pattern recognition. Digital image processing. Computational geometry Radio frequency Spatial databases Support vector machine classification Support vector machines
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	692 Vol. 1
container_issue
container_start_page	689
container_title
container_volume	1
creator	Kherfi, M.L. Ziou, D.
description	We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.
doi_str_mv	10.1109/ICIP.2004.1418848
format	conference_proceeding
fullrecord	<record><control><sourceid>pascalfrancis_6IE</sourceid><recordid>TN_cdi_pascalfrancis_primary_17611522</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1418848</ieee_id><sourcerecordid>17611522</sourcerecordid><originalsourceid>FETCH-LOGICAL-i205t-d655e7da18b5af99d62adc9541cead2ccfbc5d68ba3af687f06f6cc4b25e789a3</originalsourceid><addsrcrecordid>eNpFkE1Lw0AQhhc_wLb6A8RLLh5Td_Yrk2MpVgMFPei5THZn62oaSxIV_72RCp7ew_PMMPMKcQlyDiDLm2pZPc6VlGYOBhANHomJ0gg5WlMei6ksUGq01ugTMQGrVG4Q5ZmY9v2rlEqCholYVDvactbx0CX-pCarqeeQvbdZZBo-Os6-OG1fhtRuM2rDKDaj1noeOYea_Nu5OI3U9HzxlzPxvLp9Wt7n64e7arlY50lJO-TBWctFIMDaUizL4BQFX1oDniko72PtbXBYk6bosIjSRee9qdU4hiXpmbg-7N1T76mJ3XhF6jf7Lu2o-95A4eD3ydG7OniJmf_xoSL9A_AMWSk</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Image retrieval based on feature weighting and relevance feedback</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Kherfi, M.L. ; Ziou, D.</creator><creatorcontrib>Kherfi, M.L. ; Ziou, D.</creatorcontrib><description>We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.</description><identifier>ISSN: 1522-4880</identifier><identifier>ISBN: 0780385543</identifier><identifier>ISBN: 9780780385542</identifier><identifier>EISSN: 2381-8549</identifier><identifier>DOI: 10.1109/ICIP.2004.1418848</identifier><language>eng</language><publisher>Piscataway NJ: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Bayesian methods ; Computer science; control theory; systems ; Content based retrieval ; Exact sciences and technology ; Feedback ; Image databases ; Image retrieval ; Information retrieval ; Pattern recognition. Digital image processing. Computational geometry ; Radio frequency ; Spatial databases ; Support vector machine classification ; Support vector machines</subject><ispartof>2004 International Conference on Image Processing, 2004. ICIP '04, 2004, Vol.1, p.689-692 Vol. 1</ispartof><rights>2006 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1418848$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1418848$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17611522$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kherfi, M.L.</creatorcontrib><creatorcontrib>Ziou, D.</creatorcontrib><title>Image retrieval based on feature weighting and relevance feedback</title><title>2004 International Conference on Image Processing, 2004. ICIP '04</title><addtitle>ICIP</addtitle><description>We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Bayesian methods</subject><subject>Computer science; control theory; systems</subject><subject>Content based retrieval</subject><subject>Exact sciences and technology</subject><subject>Feedback</subject><subject>Image databases</subject><subject>Image retrieval</subject><subject>Information retrieval</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Radio frequency</subject><subject>Spatial databases</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>0780385543</isbn><isbn>9780780385542</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFkE1Lw0AQhhc_wLb6A8RLLh5Td_Yrk2MpVgMFPei5THZn62oaSxIV_72RCp7ew_PMMPMKcQlyDiDLm2pZPc6VlGYOBhANHomJ0gg5WlMei6ksUGq01ugTMQGrVG4Q5ZmY9v2rlEqCholYVDvactbx0CX-pCarqeeQvbdZZBo-Os6-OG1fhtRuM2rDKDaj1noeOYea_Nu5OI3U9HzxlzPxvLp9Wt7n64e7arlY50lJO-TBWctFIMDaUizL4BQFX1oDniko72PtbXBYk6bosIjSRee9qdU4hiXpmbg-7N1T76mJ3XhF6jf7Lu2o-95A4eD3ydG7OniJmf_xoSL9A_AMWSk</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Kherfi, M.L.</creator><creator>Ziou, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Image retrieval based on feature weighting and relevance feedback</title><author>Kherfi, M.L. ; Ziou, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i205t-d655e7da18b5af99d62adc9541cead2ccfbc5d68ba3af687f06f6cc4b25e789a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Bayesian methods</topic><topic>Computer science; control theory; systems</topic><topic>Content based retrieval</topic><topic>Exact sciences and technology</topic><topic>Feedback</topic><topic>Image databases</topic><topic>Image retrieval</topic><topic>Information retrieval</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Radio frequency</topic><topic>Spatial databases</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Kherfi, M.L.</creatorcontrib><creatorcontrib>Ziou, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kherfi, M.L.</au><au>Ziou, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Image retrieval based on feature weighting and relevance feedback</atitle><btitle>2004 International Conference on Image Processing, 2004. ICIP '04</btitle><stitle>ICIP</stitle><date>2004</date><risdate>2004</risdate><volume>1</volume><spage>689</spage><epage>692 Vol. 1</epage><pages>689-692 Vol. 1</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>0780385543</isbn><isbn>9780780385542</isbn><abstract>We present a relevance feedback model for CBIR, based on a feature weighting algorithm. The proposed model uses positive and negative items selected by the user to learn the importance of image features, then applies the obtained weights to define similarity measures corresponding to the user's perception. The basic principle of this work is to give more importance to features with a high likelihood and those which separate well between positive example (PE) classes and negative example (NE) classes. The proposed algorithm was validated separately and in the image retrieval context, and the experiments show that it contributes in improving retrieval effectiveness.</abstract><cop>Piscataway NJ</cop><pub>IEEE</pub><doi>10.1109/ICIP.2004.1418848</doi></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1522-4880
ispartof	2004 International Conference on Image Processing, 2004. ICIP '04, 2004, Vol.1, p.689-692 Vol. 1
issn	1522-4880 2381-8549
language	eng
recordid	cdi_pascalfrancis_primary_17611522
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Applied sciences Artificial intelligence Bayesian methods Computer science control theory systems Content based retrieval Exact sciences and technology Feedback Image databases Image retrieval Information retrieval Pattern recognition. Digital image processing. Computational geometry Radio frequency Spatial databases Support vector machine classification Support vector machines
title	Image retrieval based on feature weighting and relevance feedback
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T07%3A34%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Image%20retrieval%20based%20on%20feature%20weighting%20and%20relevance%20feedback&rft.btitle=2004%20International%20Conference%20on%20Image%20Processing,%202004.%20ICIP%20'04&rft.au=Kherfi,%20M.L.&rft.date=2004&rft.volume=1&rft.spage=689&rft.epage=692%20Vol.%201&rft.pages=689-692%20Vol.%201&rft.issn=1522-4880&rft.eissn=2381-8549&rft.isbn=0780385543&rft.isbn_list=9780780385542&rft_id=info:doi/10.1109/ICIP.2004.1418848&rft_dat=%3Cpascalfrancis_6IE%3E17611522%3C/pascalfrancis_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i205t-d655e7da18b5af99d62adc9541cead2ccfbc5d68ba3af687f06f6cc4b25e789a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1418848&rfr_iscdi=true