Visual learning by coevolutionary feature synthesis
In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on cybernetics 2005-06, Vol.35 (3), p.409-425 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 425 |
|---|---|
| container_issue | 3 |
| container_start_page | 409 |
| container_title | IEEE transactions on cybernetics |
| container_volume | 35 |
| creator | Krawiec, K. Bhanu, B. |
| description | In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions. |
| doi_str_mv | 10.1109/TSMCB.2005.846644 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_67961821</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1430827</ieee_id><sourcerecordid>896213917</sourcerecordid><originalsourceid>FETCH-LOGICAL-c506t-2ba3916cd7cd07b6a3a595262d53b1e6fcaa16ea2e692d213059d9fc31f9a79b3</originalsourceid><addsrcrecordid>eNqFkcFq20AQhpeSUjtuH6AUisghPcmd2V3tao6JSZqCQw5xe11W0qiRkSVHKwX89pFrQyCH5LQD-_0_w3xCfEWYIwL9XN3fLi7nEiCZp9oYrT-IKZLGGDTJk3GGVMVaI03EaQhrACAg-0lMMCGLhDgV6m8VBl9HNfuuqZp_UbaL8paf2nroq7bx3S4q2fdDx1HYNf0Dhyp8Fh9LXwf-cnxn4s_11WpxEy_vfv1eXCzjPAHTxzLzitDkhc0LsJnxyieUSCOLRGXIpsy9R8NesiFZSFSQUEFlrrAkbylTM_Hj0Lvt2seBQ-82Vci5rn3D7RBcSmZMEdqRPH-TNJYMpiP8HigJ0oQI3gdTIIl633j2Cly3Q9eMd3GpscZqo_b74QHKuzaEjku37arNeFyH4PYq3X-Vbq_SHVSOme_H4iHbcPGSOLobgW8HoGLml2-tIJVWPQMjqaD5</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>867674637</pqid></control><display><type>article</type><title>Visual learning by coevolutionary feature synthesis</title><source>IEEE Electronic Library (IEL)</source><creator>Krawiec, K. ; Bhanu, B.</creator><creatorcontrib>Krawiec, K. ; Bhanu, B.</creatorcontrib><description>In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.</description><identifier>ISSN: 1083-4419</identifier><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 1941-0492</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TSMCB.2005.846644</identifier><identifier>PMID: 15971911</identifier><identifier>CODEN: ITSCFI</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Artificial Intelligence ; Automatic programming ; Cluster Analysis ; Computer architecture ; Feature extraction ; Feature recognition ; genetic algorithms ; Genetic programming ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image recognition ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Learning ; Learning systems ; Object recognition ; pattern recognition ; Pattern Recognition, Automated - methods ; Recognition ; Signal Processing, Computer-Assisted ; Subtraction Technique ; Synthesis ; Synthetic aperture radar ; Training data ; Visual</subject><ispartof>IEEE transactions on cybernetics, 2005-06, Vol.35 (3), p.409-425</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c506t-2ba3916cd7cd07b6a3a595262d53b1e6fcaa16ea2e692d213059d9fc31f9a79b3</citedby><cites>FETCH-LOGICAL-c506t-2ba3916cd7cd07b6a3a595262d53b1e6fcaa16ea2e692d213059d9fc31f9a79b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1430827$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1430827$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15971911$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Krawiec, K.</creatorcontrib><creatorcontrib>Bhanu, B.</creatorcontrib><title>Visual learning by coevolutionary feature synthesis</title><title>IEEE transactions on cybernetics</title><addtitle>TSMCB</addtitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><description>In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Automatic programming</subject><subject>Cluster Analysis</subject><subject>Computer architecture</subject><subject>Feature extraction</subject><subject>Feature recognition</subject><subject>genetic algorithms</subject><subject>Genetic programming</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image recognition</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Learning</subject><subject>Learning systems</subject><subject>Object recognition</subject><subject>pattern recognition</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Recognition</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Subtraction Technique</subject><subject>Synthesis</subject><subject>Synthetic aperture radar</subject><subject>Training data</subject><subject>Visual</subject><issn>1083-4419</issn><issn>2168-2267</issn><issn>1941-0492</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkcFq20AQhpeSUjtuH6AUisghPcmd2V3tao6JSZqCQw5xe11W0qiRkSVHKwX89pFrQyCH5LQD-_0_w3xCfEWYIwL9XN3fLi7nEiCZp9oYrT-IKZLGGDTJk3GGVMVaI03EaQhrACAg-0lMMCGLhDgV6m8VBl9HNfuuqZp_UbaL8paf2nroq7bx3S4q2fdDx1HYNf0Dhyp8Fh9LXwf-cnxn4s_11WpxEy_vfv1eXCzjPAHTxzLzitDkhc0LsJnxyieUSCOLRGXIpsy9R8NesiFZSFSQUEFlrrAkbylTM_Hj0Lvt2seBQ-82Vci5rn3D7RBcSmZMEdqRPH-TNJYMpiP8HigJ0oQI3gdTIIl633j2Cly3Q9eMd3GpscZqo_b74QHKuzaEjku37arNeFyH4PYq3X-Vbq_SHVSOme_H4iHbcPGSOLobgW8HoGLml2-tIJVWPQMjqaD5</recordid><startdate>20050601</startdate><enddate>20050601</enddate><creator>Krawiec, K.</creator><creator>Bhanu, B.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>20050601</creationdate><title>Visual learning by coevolutionary feature synthesis</title><author>Krawiec, K. ; Bhanu, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c506t-2ba3916cd7cd07b6a3a595262d53b1e6fcaa16ea2e692d213059d9fc31f9a79b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Automatic programming</topic><topic>Cluster Analysis</topic><topic>Computer architecture</topic><topic>Feature extraction</topic><topic>Feature recognition</topic><topic>genetic algorithms</topic><topic>Genetic programming</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image recognition</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Learning</topic><topic>Learning systems</topic><topic>Object recognition</topic><topic>pattern recognition</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Recognition</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Subtraction Technique</topic><topic>Synthesis</topic><topic>Synthetic aperture radar</topic><topic>Training data</topic><topic>Visual</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Krawiec, K.</creatorcontrib><creatorcontrib>Bhanu, B.</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace 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 cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Krawiec, K.</au><au>Bhanu, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Visual learning by coevolutionary feature synthesis</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TSMCB</stitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><date>2005-06-01</date><risdate>2005</risdate><volume>35</volume><issue>3</issue><spage>409</spage><epage>425</epage><pages>409-425</pages><issn>1083-4419</issn><issn>2168-2267</issn><eissn>1941-0492</eissn><eissn>2168-2275</eissn><coden>ITSCFI</coden><abstract>In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>15971911</pmid><doi>10.1109/TSMCB.2005.846644</doi><tpages>17</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1083-4419 |
| ispartof | IEEE transactions on cybernetics, 2005-06, Vol.35 (3), p.409-425 |
| issn | 1083-4419 2168-2267 1941-0492 2168-2275 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_67961821 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Artificial Intelligence Automatic programming Cluster Analysis Computer architecture Feature extraction Feature recognition genetic algorithms Genetic programming Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image recognition Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Learning Learning systems Object recognition pattern recognition Pattern Recognition, Automated - methods Recognition Signal Processing, Computer-Assisted Subtraction Technique Synthesis Synthetic aperture radar Training data Visual |
| title | Visual learning by coevolutionary feature synthesis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T12%3A44%3A30IST&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=Visual%20learning%20by%20coevolutionary%20feature%20synthesis&rft.jtitle=IEEE%20transactions%20on%20cybernetics&rft.au=Krawiec,%20K.&rft.date=2005-06-01&rft.volume=35&rft.issue=3&rft.spage=409&rft.epage=425&rft.pages=409-425&rft.issn=1083-4419&rft.eissn=1941-0492&rft.coden=ITSCFI&rft_id=info:doi/10.1109/TSMCB.2005.846644&rft_dat=%3Cproquest_RIE%3E896213917%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=867674637&rft_id=info:pmid/15971911&rft_ieee_id=1430827&rfr_iscdi=true |