A Region-Based SRG Algorithm for Color Image Segmentation
In this paper, we present an automatic seeded region growing (SRG) algorithm for color image segmentation. The method uses regions rather than pixels as the seeds of SRG. The architecture of the algorithm can be described as follows. First, the input RGB color image is transformed into HSI color spa...
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 | 1547 |
|---|---|
| container_issue | |
| container_start_page | 1542 |
| container_title | |
| container_volume | 3 |
| creator | Jia-Nan Wang Jun Kong Ying-Hua Lu Wen-Xiang Gu Ming-Hao Yin Yong-Peng Xiao |
| description | In this paper, we present an automatic seeded region growing (SRG) algorithm for color image segmentation. The method uses regions rather than pixels as the seeds of SRG. The architecture of the algorithm can be described as follows. First, the input RGB color image is transformed into HSI color space. Second, we use watershed segmentation to initialize the image. Third, the initial region seeds are automatically selected according to two rules advanced by us. Fourth, the color image is segmented into regions. Finally, region-merging method is used to merge similar or small regions. Compared with pixel-based SRG algorithm, our method can yield more robust and precise results. Experimental results have also shown that our algorithm can produce excellent results. |
| doi_str_mv | 10.1109/ICMLC.2007.4370390 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4370390</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4370390</ieee_id><sourcerecordid>4370390</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-82eb77ead5bb87574c65417c9638041c15382c64ae5552649d58d60fece883953</originalsourceid><addsrcrecordid>eNo1j81Kw0AUhUdUsNa8gG7mBRLvnf9ZxqA1EBFaBXdlktzESNJIko1vb8G6OYdvcT44jN0iJIjg7_PspcgSAWATJS1ID2cs8tahEkqBtxLO2fU_CLxgK4EGYpTy44pF8_wFAGiNAiFXzKd8S203HuKHMFPNd9sNT_t2nLrlc-DNOPFs7I-ZD6ElvqN2oMMSluPghl02oZ8pOvWavT89vmXPcfG6ybO0iDu0eomdoNJaCrUuS2e1VZXRCm3ljXSgsEItnaiMCqS1Fkb5WrvaQEMVOSe9lmt29-ftiGj_PXVDmH72p-fyF6RQR8w</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A Region-Based SRG Algorithm for Color Image Segmentation</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Jia-Nan Wang ; Jun Kong ; Ying-Hua Lu ; Wen-Xiang Gu ; Ming-Hao Yin ; Yong-Peng Xiao</creator><creatorcontrib>Jia-Nan Wang ; Jun Kong ; Ying-Hua Lu ; Wen-Xiang Gu ; Ming-Hao Yin ; Yong-Peng Xiao</creatorcontrib><description>In this paper, we present an automatic seeded region growing (SRG) algorithm for color image segmentation. The method uses regions rather than pixels as the seeds of SRG. The architecture of the algorithm can be described as follows. First, the input RGB color image is transformed into HSI color space. Second, we use watershed segmentation to initialize the image. Third, the initial region seeds are automatically selected according to two rules advanced by us. Fourth, the color image is segmented into regions. Finally, region-merging method is used to merge similar or small regions. Compared with pixel-based SRG algorithm, our method can yield more robust and precise results. Experimental results have also shown that our algorithm can produce excellent results.</description><identifier>ISSN: 2160-133X</identifier><identifier>ISBN: 1424409721</identifier><identifier>ISBN: 9781424409723</identifier><identifier>EISBN: 9781424409730</identifier><identifier>EISBN: 142440973X</identifier><identifier>DOI: 10.1109/ICMLC.2007.4370390</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automatic seeded region growing ; Color image processing ; Computer science ; Cybernetics ; Image color analysis ; Image edge detection ; Image segmentation ; Image texture analysis ; Machine learning ; Partitioning algorithms ; Pattern recognition ; Pixel ; SRG ; Watershed segmentation</subject><ispartof>2007 International Conference on Machine Learning and Cybernetics, 2007, Vol.3, p.1542-1547</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/4370390$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,778,782,787,788,2054,27912,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4370390$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jia-Nan Wang</creatorcontrib><creatorcontrib>Jun Kong</creatorcontrib><creatorcontrib>Ying-Hua Lu</creatorcontrib><creatorcontrib>Wen-Xiang Gu</creatorcontrib><creatorcontrib>Ming-Hao Yin</creatorcontrib><creatorcontrib>Yong-Peng Xiao</creatorcontrib><title>A Region-Based SRG Algorithm for Color Image Segmentation</title><title>2007 International Conference on Machine Learning and Cybernetics</title><addtitle>ICMLC</addtitle><description>In this paper, we present an automatic seeded region growing (SRG) algorithm for color image segmentation. The method uses regions rather than pixels as the seeds of SRG. The architecture of the algorithm can be described as follows. First, the input RGB color image is transformed into HSI color space. Second, we use watershed segmentation to initialize the image. Third, the initial region seeds are automatically selected according to two rules advanced by us. Fourth, the color image is segmented into regions. Finally, region-merging method is used to merge similar or small regions. Compared with pixel-based SRG algorithm, our method can yield more robust and precise results. Experimental results have also shown that our algorithm can produce excellent results.</description><subject>Automatic seeded region growing</subject><subject>Color image processing</subject><subject>Computer science</subject><subject>Cybernetics</subject><subject>Image color analysis</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Image texture analysis</subject><subject>Machine learning</subject><subject>Partitioning algorithms</subject><subject>Pattern recognition</subject><subject>Pixel</subject><subject>SRG</subject><subject>Watershed segmentation</subject><issn>2160-133X</issn><isbn>1424409721</isbn><isbn>9781424409723</isbn><isbn>9781424409730</isbn><isbn>142440973X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81Kw0AUhUdUsNa8gG7mBRLvnf9ZxqA1EBFaBXdlktzESNJIko1vb8G6OYdvcT44jN0iJIjg7_PspcgSAWATJS1ID2cs8tahEkqBtxLO2fU_CLxgK4EGYpTy44pF8_wFAGiNAiFXzKd8S203HuKHMFPNd9sNT_t2nLrlc-DNOPFs7I-ZD6ElvqN2oMMSluPghl02oZ8pOvWavT89vmXPcfG6ybO0iDu0eomdoNJaCrUuS2e1VZXRCm3ljXSgsEItnaiMCqS1Fkb5WrvaQEMVOSe9lmt29-ftiGj_PXVDmH72p-fyF6RQR8w</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Jia-Nan Wang</creator><creator>Jun Kong</creator><creator>Ying-Hua Lu</creator><creator>Wen-Xiang Gu</creator><creator>Ming-Hao Yin</creator><creator>Yong-Peng Xiao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>A Region-Based SRG Algorithm for Color Image Segmentation</title><author>Jia-Nan Wang ; Jun Kong ; Ying-Hua Lu ; Wen-Xiang Gu ; Ming-Hao Yin ; Yong-Peng Xiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-82eb77ead5bb87574c65417c9638041c15382c64ae5552649d58d60fece883953</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Automatic seeded region growing</topic><topic>Color image processing</topic><topic>Computer science</topic><topic>Cybernetics</topic><topic>Image color analysis</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Image texture analysis</topic><topic>Machine learning</topic><topic>Partitioning algorithms</topic><topic>Pattern recognition</topic><topic>Pixel</topic><topic>SRG</topic><topic>Watershed segmentation</topic><toplevel>online_resources</toplevel><creatorcontrib>Jia-Nan Wang</creatorcontrib><creatorcontrib>Jun Kong</creatorcontrib><creatorcontrib>Ying-Hua Lu</creatorcontrib><creatorcontrib>Wen-Xiang Gu</creatorcontrib><creatorcontrib>Ming-Hao Yin</creatorcontrib><creatorcontrib>Yong-Peng Xiao</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>Jia-Nan Wang</au><au>Jun Kong</au><au>Ying-Hua Lu</au><au>Wen-Xiang Gu</au><au>Ming-Hao Yin</au><au>Yong-Peng Xiao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Region-Based SRG Algorithm for Color Image Segmentation</atitle><btitle>2007 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2007-08</date><risdate>2007</risdate><volume>3</volume><spage>1542</spage><epage>1547</epage><pages>1542-1547</pages><issn>2160-133X</issn><isbn>1424409721</isbn><isbn>9781424409723</isbn><eisbn>9781424409730</eisbn><eisbn>142440973X</eisbn><abstract>In this paper, we present an automatic seeded region growing (SRG) algorithm for color image segmentation. The method uses regions rather than pixels as the seeds of SRG. The architecture of the algorithm can be described as follows. First, the input RGB color image is transformed into HSI color space. Second, we use watershed segmentation to initialize the image. Third, the initial region seeds are automatically selected according to two rules advanced by us. Fourth, the color image is segmented into regions. Finally, region-merging method is used to merge similar or small regions. Compared with pixel-based SRG algorithm, our method can yield more robust and precise results. Experimental results have also shown that our algorithm can produce excellent results.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2007.4370390</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2160-133X |
| ispartof | 2007 International Conference on Machine Learning and Cybernetics, 2007, Vol.3, p.1542-1547 |
| issn | 2160-133X |
| language | eng |
| recordid | cdi_ieee_primary_4370390 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automatic seeded region growing Color image processing Computer science Cybernetics Image color analysis Image edge detection Image segmentation Image texture analysis Machine learning Partitioning algorithms Pattern recognition Pixel SRG Watershed segmentation |
| title | A Region-Based SRG Algorithm for Color Image Segmentation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T22%3A34%3A03IST&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=A%20Region-Based%20SRG%20Algorithm%20for%20Color%20Image%20Segmentation&rft.btitle=2007%20International%20Conference%20on%20Machine%20Learning%20and%20Cybernetics&rft.au=Jia-Nan%20Wang&rft.date=2007-08&rft.volume=3&rft.spage=1542&rft.epage=1547&rft.pages=1542-1547&rft.issn=2160-133X&rft.isbn=1424409721&rft.isbn_list=9781424409723&rft_id=info:doi/10.1109/ICMLC.2007.4370390&rft_dat=%3Cieee_6IE%3E4370390%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424409730&rft.eisbn_list=142440973X&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4370390&rfr_iscdi=true |