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Image segmentation method based on self-organizing maps and K-means algorithm
In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for...
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| creator | Ristic, D.M. Pavlovic, M. Reljin, I. |
| description | In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm. |
| doi_str_mv | 10.1109/NEUREL.2008.4685551 |
| format | conference_proceeding |
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The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm.</description><identifier>ISBN: 142442903X</identifier><identifier>ISBN: 9781424429035</identifier><identifier>EISBN: 1424429048</identifier><identifier>EISBN: 9781424429042</identifier><identifier>DOI: 10.1109/NEUREL.2008.4685551</identifier><identifier>LCCN: 2008935851</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Clustering algorithms ; Color ; Color image segmentation ; Data mining ; Image segmentation ; Information retrieval ; Iterative algorithms ; K-means algorithm ; Kohonen neural network ; Neural networks ; Pixel ; Self organizing feature maps ; self-organizing map</subject><ispartof>2008 9th Symposium on Neural Network Applications in Electrical Engineering, 2008, p.27-30</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c140t-3a5a4ed00070af7b96f9f3d194901e289aa1eaa8f1844ec0a373e0cde9e19acd3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4685551$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27916,54911</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4685551$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ristic, D.M.</creatorcontrib><creatorcontrib>Pavlovic, M.</creatorcontrib><creatorcontrib>Reljin, I.</creatorcontrib><title>Image segmentation method based on self-organizing maps and K-means algorithm</title><title>2008 9th Symposium on Neural Network Applications in Electrical Engineering</title><addtitle>NEUREL</addtitle><description>In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm.</description><subject>Algorithm design and analysis</subject><subject>Clustering algorithms</subject><subject>Color</subject><subject>Color image segmentation</subject><subject>Data mining</subject><subject>Image segmentation</subject><subject>Information retrieval</subject><subject>Iterative algorithms</subject><subject>K-means algorithm</subject><subject>Kohonen neural network</subject><subject>Neural networks</subject><subject>Pixel</subject><subject>Self organizing feature maps</subject><subject>self-organizing map</subject><isbn>142442903X</isbn><isbn>9781424429035</isbn><isbn>1424429048</isbn><isbn>9781424429042</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFUFFLwzAYjMhAN_cL9pI_0Jk0SZs8yqg6rAriwLfxrfnSRZp0NH3RX2_Fgfdyd3AcxxGy4mzNOTO3L9XurarXOWN6LQutlOIXZM5lLmVumNSX_0Z8zMj8N2iE0opfkWVKn2yCVKLQ-TV53gZokSZsA8YRRt9HGnA89pYeIKGlk0_YuawfWoj-28eWBjglCtHSpywgxEl3bT_48RhuyMxBl3B55gXZ3Vfvm8esfn3Ybu7qrOGSjZkABRLtNKNk4MqDKZxxwnIjDeOYawPAEUA7rqXEhoEoBbLGokFuoLFiQVZ_vR4R96fBBxi-9ucvxA_m0VHn</recordid><startdate>200809</startdate><enddate>200809</enddate><creator>Ristic, D.M.</creator><creator>Pavlovic, M.</creator><creator>Reljin, I.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200809</creationdate><title>Image segmentation method based on self-organizing maps and K-means algorithm</title><author>Ristic, D.M. ; Pavlovic, M. ; Reljin, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c140t-3a5a4ed00070af7b96f9f3d194901e289aa1eaa8f1844ec0a373e0cde9e19acd3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithm design and analysis</topic><topic>Clustering algorithms</topic><topic>Color</topic><topic>Color image segmentation</topic><topic>Data mining</topic><topic>Image segmentation</topic><topic>Information retrieval</topic><topic>Iterative algorithms</topic><topic>K-means algorithm</topic><topic>Kohonen neural network</topic><topic>Neural networks</topic><topic>Pixel</topic><topic>Self organizing feature maps</topic><topic>self-organizing map</topic><toplevel>online_resources</toplevel><creatorcontrib>Ristic, D.M.</creatorcontrib><creatorcontrib>Pavlovic, M.</creatorcontrib><creatorcontrib>Reljin, I.</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 Xplore Digital Library</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>Ristic, D.M.</au><au>Pavlovic, M.</au><au>Reljin, I.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Image segmentation method based on self-organizing maps and K-means algorithm</atitle><btitle>2008 9th Symposium on Neural Network Applications in Electrical Engineering</btitle><stitle>NEUREL</stitle><date>2008-09</date><risdate>2008</risdate><spage>27</spage><epage>30</epage><pages>27-30</pages><isbn>142442903X</isbn><isbn>9781424429035</isbn><eisbn>1424429048</eisbn><eisbn>9781424429042</eisbn><abstract>In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm.</abstract><pub>IEEE</pub><doi>10.1109/NEUREL.2008.4685551</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 142442903X |
| ispartof | 2008 9th Symposium on Neural Network Applications in Electrical Engineering, 2008, p.27-30 |
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| language | eng |
| recordid | cdi_ieee_primary_4685551 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Clustering algorithms Color Color image segmentation Data mining Image segmentation Information retrieval Iterative algorithms K-means algorithm Kohonen neural network Neural networks Pixel Self organizing feature maps self-organizing map |
| title | Image segmentation method based on self-organizing maps and K-means algorithm |
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