Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging
A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop co...
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Veröffentlicht in: | Dian zi yu xin xi xue bao = Journal of electronics & information technology 2016-07, Vol.38 (7), p.1594-1601 |
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creator | Luo, Huilan Wan, Chengtao Kong, Fansheng |
description | A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop connected graph is constructed, in which nodes are the super-pixels and the adjacent regions are expanded reasonably relying on the total number of super-pixels. Then, all the color values in each super-pixel are clustered in terms of their discriminative power to get the statistical probability distribution of the cluster labels for each super-pixel. Next, the edges between all adjacent super-pixel pairs are weighted with the harmonic-mean of KL divergence of their probability distributions, and then the multi-scale saliency maps are calculated according to boundary connectivity and region contrast. The final saliency map is obtained by calculating and optimizing the mean map of all the saliency maps with different |
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Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop connected graph is constructed, in which nodes are the super-pixels and the adjacent regions are expanded reasonably relying on the total number of super-pixels. Then, all the color values in each super-pixel are clustered in terms of their discriminative power to get the statistical probability distribution of the cluster labels for each super-pixel. Next, the edges between all adjacent super-pixel pairs are weighted with the harmonic-mean of KL divergence of their probability distributions, and then the multi-scale saliency maps are calculated according to boundary connectivity and region contrast. The final saliency map is obtained by calculating and optimizing the mean map of all the saliency maps with different</description><subject>Algorithms</subject><subject>Color</subject><subject>Divergence</subject><subject>Electronics</subject><subject>Image segmentation</subject><subject>Mathematical analysis</subject><subject>Merging</subject><issn>1009-5896</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNotjrFOwzAURT2ARFU68QMeWQJ-TmzHY9UWKKRCgjJXtvMSLLlJiZ1-P6lgukc60tEl5A7YA4DW-vF1s92DACjEFZkBYzoTpZY3ZBGjt4znIBVj-YxUnyZ47BL9wNb3HV1jQpcutAxtP_j0faRnb-hbRdf-jEOLnUNqupruxpB8Fp0JSHeT8F17S64bEyIu_ndOvp42-9VLVr0_b1fLKjsBy1NWC-DO1VLxWkslmbRlUbu6lIXSyiIWtmGWFyAsU9YgTlfBKdtwBCi5Nfmc3P91T0P_M2JMh6OPDkMwHfZjPMCUEoJLLvJfSOhPcw</recordid><startdate>20160701</startdate><enddate>20160701</enddate><creator>Luo, Huilan</creator><creator>Wan, Chengtao</creator><creator>Kong, Fansheng</creator><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160701</creationdate><title>Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging</title><author>Luo, Huilan ; Wan, Chengtao ; Kong, Fansheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p103t-d512ccd672d967606b84dcd864797bee4bf0b2415b07baee6701c7bf2e1182ba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>chi</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Color</topic><topic>Divergence</topic><topic>Electronics</topic><topic>Image segmentation</topic><topic>Mathematical analysis</topic><topic>Merging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Huilan</creatorcontrib><creatorcontrib>Wan, Chengtao</creatorcontrib><creatorcontrib>Kong, Fansheng</creatorcontrib><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><jtitle>Dian zi yu xin xi xue bao = Journal of electronics & information technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Huilan</au><au>Wan, Chengtao</au><au>Kong, Fansheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging</atitle><jtitle>Dian zi yu xin xi xue bao = Journal of electronics & information technology</jtitle><date>2016-07-01</date><risdate>2016</risdate><volume>38</volume><issue>7</issue><spage>1594</spage><epage>1601</epage><pages>1594-1601</pages><issn>1009-5896</issn><abstract>A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. 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subjects | Algorithms Color Divergence Electronics Image segmentation Mathematical analysis Merging |
title | Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging |
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