Sparse Spatial Coding: A Novel Approach to Visual Recognition
Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on image processing 2014-06, Vol.23 (6), p.2719-2731 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2731 |
---|---|
container_issue | 6 |
container_start_page | 2719 |
container_title | IEEE transactions on image processing |
container_volume | 23 |
creator | Leivas Oliveira, Gabriel Nascimento, Erickson R. Wilson Vieira, Antonio Montenegro Campos, Mario Fernando |
description | Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks. |
doi_str_mv | 10.1109/TIP.2014.2317988 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TIP_2014_2317988</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6800007</ieee_id><sourcerecordid>3377630031</sourcerecordid><originalsourceid>FETCH-LOGICAL-c377t-e1d56af8c44b837af8d15a81118e685379cc42da485c6e8afe20a7cd9e42e4cc3</originalsourceid><addsrcrecordid>eNpd0N9LwzAQB_Agij-m74IgBRF86cylSZMIPozhLxgqOn0tMb1qpGtm0wr-92ZsKpiXO8jnjuNLyD7QIQDVp9Ob-yGjwIcsA6mVWiPboDmklHK2HnsqZCqB6y2yE8I7jVJAvkm2GJc51VRvk_PHuWkDJrF0ztTJ2JeueT1LRsmt_8Q6Gc3nrTf2Lel88uxCH8kDWv_auM75ZpdsVKYOuLeqA_J0eTEdX6eTu6ub8WiS2kzKLkUoRW4qZTl_UZmMXQnCKABQmCuRSW0tZ6XhStgclamQUSNtqZEz5NZmA3Ky3BuP-egxdMXMBYt1bRr0fShAZIJRyVke6dE_-u77tonXRcWFloxRHhVdKtv6EFqsinnrZqb9KoAWi2iLGG2xiLZYRRtHDleL-5cZlr8DP1lGcLwCJlhTV61prAt_TgmmclgsOlg6h4i_37mi8cnsGyorh50</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1545972204</pqid></control><display><type>article</type><title>Sparse Spatial Coding: A Novel Approach to Visual Recognition</title><source>IEEE Electronic Library (IEL)</source><creator>Leivas Oliveira, Gabriel ; Nascimento, Erickson R. ; Wilson Vieira, Antonio ; Montenegro Campos, Mario Fernando</creator><creatorcontrib>Leivas Oliveira, Gabriel ; Nascimento, Erickson R. ; Wilson Vieira, Antonio ; Montenegro Campos, Mario Fernando</creatorcontrib><description>Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2014.2317988</identifier><identifier>PMID: 24760909</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Accuracy ; Applied sciences ; Coding, codes ; Dictionaries ; Encoding ; Exact sciences and technology ; Feature extraction ; Image coding ; Image processing ; Information, signal and communications theory ; Object recognition ; Pattern recognition ; Physiological psychology ; Sampling, quantization ; Signal and communications theory ; Signal processing ; Telecommunications and information theory ; Vectors</subject><ispartof>IEEE transactions on image processing, 2014-06, Vol.23 (6), p.2719-2731</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c377t-e1d56af8c44b837af8d15a81118e685379cc42da485c6e8afe20a7cd9e42e4cc3</citedby><cites>FETCH-LOGICAL-c377t-e1d56af8c44b837af8d15a81118e685379cc42da485c6e8afe20a7cd9e42e4cc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6800007$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6800007$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28528618$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24760909$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leivas Oliveira, Gabriel</creatorcontrib><creatorcontrib>Nascimento, Erickson R.</creatorcontrib><creatorcontrib>Wilson Vieira, Antonio</creatorcontrib><creatorcontrib>Montenegro Campos, Mario Fernando</creatorcontrib><title>Sparse Spatial Coding: A Novel Approach to Visual Recognition</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks.</description><subject>Accuracy</subject><subject>Applied sciences</subject><subject>Coding, codes</subject><subject>Dictionaries</subject><subject>Encoding</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Image coding</subject><subject>Image processing</subject><subject>Information, signal and communications theory</subject><subject>Object recognition</subject><subject>Pattern recognition</subject><subject>Physiological psychology</subject><subject>Sampling, quantization</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Telecommunications and information theory</subject><subject>Vectors</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpd0N9LwzAQB_Agij-m74IgBRF86cylSZMIPozhLxgqOn0tMb1qpGtm0wr-92ZsKpiXO8jnjuNLyD7QIQDVp9Ob-yGjwIcsA6mVWiPboDmklHK2HnsqZCqB6y2yE8I7jVJAvkm2GJc51VRvk_PHuWkDJrF0ztTJ2JeueT1LRsmt_8Q6Gc3nrTf2Lel88uxCH8kDWv_auM75ZpdsVKYOuLeqA_J0eTEdX6eTu6ub8WiS2kzKLkUoRW4qZTl_UZmMXQnCKABQmCuRSW0tZ6XhStgclamQUSNtqZEz5NZmA3Ky3BuP-egxdMXMBYt1bRr0fShAZIJRyVke6dE_-u77tonXRcWFloxRHhVdKtv6EFqsinnrZqb9KoAWi2iLGG2xiLZYRRtHDleL-5cZlr8DP1lGcLwCJlhTV61prAt_TgmmclgsOlg6h4i_37mi8cnsGyorh50</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Leivas Oliveira, Gabriel</creator><creator>Nascimento, Erickson R.</creator><creator>Wilson Vieira, Antonio</creator><creator>Montenegro Campos, Mario Fernando</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>20140601</creationdate><title>Sparse Spatial Coding: A Novel Approach to Visual Recognition</title><author>Leivas Oliveira, Gabriel ; Nascimento, Erickson R. ; Wilson Vieira, Antonio ; Montenegro Campos, Mario Fernando</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c377t-e1d56af8c44b837af8d15a81118e685379cc42da485c6e8afe20a7cd9e42e4cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Accuracy</topic><topic>Applied sciences</topic><topic>Coding, codes</topic><topic>Dictionaries</topic><topic>Encoding</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>Image coding</topic><topic>Image processing</topic><topic>Information, signal and communications theory</topic><topic>Object recognition</topic><topic>Pattern recognition</topic><topic>Physiological psychology</topic><topic>Sampling, quantization</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Telecommunications and information theory</topic><topic>Vectors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leivas Oliveira, Gabriel</creatorcontrib><creatorcontrib>Nascimento, Erickson R.</creatorcontrib><creatorcontrib>Wilson Vieira, Antonio</creatorcontrib><creatorcontrib>Montenegro Campos, Mario Fernando</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>PubMed</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>MEDLINE - Academic</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Leivas Oliveira, Gabriel</au><au>Nascimento, Erickson R.</au><au>Wilson Vieira, Antonio</au><au>Montenegro Campos, Mario Fernando</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sparse Spatial Coding: A Novel Approach to Visual Recognition</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2014-06-01</date><risdate>2014</risdate><volume>23</volume><issue>6</issue><spage>2719</spage><epage>2731</epage><pages>2719-2731</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>24760909</pmid><doi>10.1109/TIP.2014.2317988</doi><tpages>13</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1057-7149 |
ispartof | IEEE transactions on image processing, 2014-06, Vol.23 (6), p.2719-2731 |
issn | 1057-7149 1941-0042 |
language | eng |
recordid | cdi_crossref_primary_10_1109_TIP_2014_2317988 |
source | IEEE Electronic Library (IEL) |
subjects | Accuracy Applied sciences Coding, codes Dictionaries Encoding Exact sciences and technology Feature extraction Image coding Image processing Information, signal and communications theory Object recognition Pattern recognition Physiological psychology Sampling, quantization Signal and communications theory Signal processing Telecommunications and information theory Vectors |
title | Sparse Spatial Coding: A Novel Approach to Visual Recognition |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T09%3A48%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Sparse%20Spatial%20Coding:%20A%20Novel%20Approach%20to%20Visual%20Recognition&rft.jtitle=IEEE%20transactions%20on%20image%20processing&rft.au=Leivas%20Oliveira,%20Gabriel&rft.date=2014-06-01&rft.volume=23&rft.issue=6&rft.spage=2719&rft.epage=2731&rft.pages=2719-2731&rft.issn=1057-7149&rft.eissn=1941-0042&rft.coden=IIPRE4&rft_id=info:doi/10.1109/TIP.2014.2317988&rft_dat=%3Cproquest_RIE%3E3377630031%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1545972204&rft_id=info:pmid/24760909&rft_ieee_id=6800007&rfr_iscdi=true |