Key point reduction in SIFT descriptor used by subtractive clustering
The SIFT descriptor is one of the most widely used descriptors and is very stable in regard to changes in rotation, scale, affine, illumination, etc. This method is based on key points extracted from the image. If there are many such points, a lot of time will be needed in the matching and recogniti...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 911 |
|---|---|
| container_issue | |
| container_start_page | 906 |
| container_title | |
| container_volume | |
| creator | Alitappeh, R. J. Saravi, K. J. Mahmoudi, F. |
| description | The SIFT descriptor is one of the most widely used descriptors and is very stable in regard to changes in rotation, scale, affine, illumination, etc. This method is based on key points extracted from the image. If there are many such points, a lot of time will be needed in the matching and recognition phases. For this reason, we have tried in this article to use the clustering technique in order to reduce the number of key points by omitting similar points. In other words, subtractive clustering is used to select key points which are more distinct from and less similar to other points. In the section on conclusions, a successful implementation of this method is presented. The efficiencies of the proposed algorithm and of the base SIFT algorithm on the data set ALOI were investigated and it was observed that by adding this method to the base SIFT descriptor the rate of recognition increases by two percent and the time complexity decreases by 1.035728 seconds. |
| doi_str_mv | 10.1109/ISSPA.2012.6310683 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6310683</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6310683</ieee_id><sourcerecordid>6310683</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-5438ac63a7211c47eb7b36dd28fe5eb6806529f6e044f68afb4eff85127878fc3</originalsourceid><addsrcrecordid>eNpVj9FKwzAYhSMiKLMvoDd5gdakSZO_l2NsOhwotPcjTf9IZLYlSYW-vQN3s3Nz-ODwwSHkibOCc1a_7Jvmc12UjJeFEpwpEDckqzVwqbRgApi8vWLO70kW4zc7BzhIBg9k-44LnUY_JBqwn23y40D9QJv9rqU9Rhv8lMZA54g97RYa5y4Fc579IrWnOSYMfvh6JHfOnCJml16RdrdtN2_54eN1v1kfcl-zlFdSgLFKGF1ybqXGTndC9X0JDivsFDBVlbVTyKR0CozrJDoHFS81aHBWrMjzv9Yj4nEK_seE5Xj5Lv4APqxNLQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Key point reduction in SIFT descriptor used by subtractive clustering</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Alitappeh, R. J. ; Saravi, K. J. ; Mahmoudi, F.</creator><creatorcontrib>Alitappeh, R. J. ; Saravi, K. J. ; Mahmoudi, F.</creatorcontrib><description>The SIFT descriptor is one of the most widely used descriptors and is very stable in regard to changes in rotation, scale, affine, illumination, etc. This method is based on key points extracted from the image. If there are many such points, a lot of time will be needed in the matching and recognition phases. For this reason, we have tried in this article to use the clustering technique in order to reduce the number of key points by omitting similar points. In other words, subtractive clustering is used to select key points which are more distinct from and less similar to other points. In the section on conclusions, a successful implementation of this method is presented. The efficiencies of the proposed algorithm and of the base SIFT algorithm on the data set ALOI were investigated and it was observed that by adding this method to the base SIFT descriptor the rate of recognition increases by two percent and the time complexity decreases by 1.035728 seconds.</description><identifier>ISBN: 9781467303811</identifier><identifier>ISBN: 146730381X</identifier><identifier>EISBN: 9781467303804</identifier><identifier>EISBN: 1467303801</identifier><identifier>EISBN: 1467303828</identifier><identifier>EISBN: 9781467303828</identifier><identifier>DOI: 10.1109/ISSPA.2012.6310683</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Clustering algorithms ; Data mining ; Feature extraction ; Object recognition ; SIFT Descriptor ; Subtractive Clustering ; Training ; Vectors</subject><ispartof>2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, p.906-911</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/6310683$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6310683$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Alitappeh, R. J.</creatorcontrib><creatorcontrib>Saravi, K. J.</creatorcontrib><creatorcontrib>Mahmoudi, F.</creatorcontrib><title>Key point reduction in SIFT descriptor used by subtractive clustering</title><title>2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)</title><addtitle>ISSPA</addtitle><description>The SIFT descriptor is one of the most widely used descriptors and is very stable in regard to changes in rotation, scale, affine, illumination, etc. This method is based on key points extracted from the image. If there are many such points, a lot of time will be needed in the matching and recognition phases. For this reason, we have tried in this article to use the clustering technique in order to reduce the number of key points by omitting similar points. In other words, subtractive clustering is used to select key points which are more distinct from and less similar to other points. In the section on conclusions, a successful implementation of this method is presented. The efficiencies of the proposed algorithm and of the base SIFT algorithm on the data set ALOI were investigated and it was observed that by adding this method to the base SIFT descriptor the rate of recognition increases by two percent and the time complexity decreases by 1.035728 seconds.</description><subject>Accuracy</subject><subject>Clustering algorithms</subject><subject>Data mining</subject><subject>Feature extraction</subject><subject>Object recognition</subject><subject>SIFT Descriptor</subject><subject>Subtractive Clustering</subject><subject>Training</subject><subject>Vectors</subject><isbn>9781467303811</isbn><isbn>146730381X</isbn><isbn>9781467303804</isbn><isbn>1467303801</isbn><isbn>1467303828</isbn><isbn>9781467303828</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVj9FKwzAYhSMiKLMvoDd5gdakSZO_l2NsOhwotPcjTf9IZLYlSYW-vQN3s3Nz-ODwwSHkibOCc1a_7Jvmc12UjJeFEpwpEDckqzVwqbRgApi8vWLO70kW4zc7BzhIBg9k-44LnUY_JBqwn23y40D9QJv9rqU9Rhv8lMZA54g97RYa5y4Fc579IrWnOSYMfvh6JHfOnCJml16RdrdtN2_54eN1v1kfcl-zlFdSgLFKGF1ybqXGTndC9X0JDivsFDBVlbVTyKR0CozrJDoHFS81aHBWrMjzv9Yj4nEK_seE5Xj5Lv4APqxNLQ</recordid><startdate>201207</startdate><enddate>201207</enddate><creator>Alitappeh, R. J.</creator><creator>Saravi, K. J.</creator><creator>Mahmoudi, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201207</creationdate><title>Key point reduction in SIFT descriptor used by subtractive clustering</title><author>Alitappeh, R. J. ; Saravi, K. J. ; Mahmoudi, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-5438ac63a7211c47eb7b36dd28fe5eb6806529f6e044f68afb4eff85127878fc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Clustering algorithms</topic><topic>Data mining</topic><topic>Feature extraction</topic><topic>Object recognition</topic><topic>SIFT Descriptor</topic><topic>Subtractive Clustering</topic><topic>Training</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Alitappeh, R. J.</creatorcontrib><creatorcontrib>Saravi, K. J.</creatorcontrib><creatorcontrib>Mahmoudi, F.</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 Electronic Library (IEL)</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>Alitappeh, R. J.</au><au>Saravi, K. J.</au><au>Mahmoudi, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Key point reduction in SIFT descriptor used by subtractive clustering</atitle><btitle>2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)</btitle><stitle>ISSPA</stitle><date>2012-07</date><risdate>2012</risdate><spage>906</spage><epage>911</epage><pages>906-911</pages><isbn>9781467303811</isbn><isbn>146730381X</isbn><eisbn>9781467303804</eisbn><eisbn>1467303801</eisbn><eisbn>1467303828</eisbn><eisbn>9781467303828</eisbn><abstract>The SIFT descriptor is one of the most widely used descriptors and is very stable in regard to changes in rotation, scale, affine, illumination, etc. This method is based on key points extracted from the image. If there are many such points, a lot of time will be needed in the matching and recognition phases. For this reason, we have tried in this article to use the clustering technique in order to reduce the number of key points by omitting similar points. In other words, subtractive clustering is used to select key points which are more distinct from and less similar to other points. In the section on conclusions, a successful implementation of this method is presented. The efficiencies of the proposed algorithm and of the base SIFT algorithm on the data set ALOI were investigated and it was observed that by adding this method to the base SIFT descriptor the rate of recognition increases by two percent and the time complexity decreases by 1.035728 seconds.</abstract><pub>IEEE</pub><doi>10.1109/ISSPA.2012.6310683</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781467303811 |
| ispartof | 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, p.906-911 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6310683 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Clustering algorithms Data mining Feature extraction Object recognition SIFT Descriptor Subtractive Clustering Training Vectors |
| title | Key point reduction in SIFT descriptor used by subtractive clustering |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T19%3A40%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Key%20point%20reduction%20in%20SIFT%20descriptor%20used%20by%20subtractive%20clustering&rft.btitle=2012%2011th%20International%20Conference%20on%20Information%20Science,%20Signal%20Processing%20and%20their%20Applications%20(ISSPA)&rft.au=Alitappeh,%20R.%20J.&rft.date=2012-07&rft.spage=906&rft.epage=911&rft.pages=906-911&rft.isbn=9781467303811&rft.isbn_list=146730381X&rft_id=info:doi/10.1109/ISSPA.2012.6310683&rft_dat=%3Cieee_6IE%3E6310683%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467303804&rft.eisbn_list=1467303801&rft.eisbn_list=1467303828&rft.eisbn_list=9781467303828&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6310683&rfr_iscdi=true |