A Universal Framework for Salient Object Detection
In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. First, rough salient regions are extracted from any existing saliency detection model with distance weighting, adaptive binarization, a...
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| Veröffentlicht in: | IEEE transactions on multimedia 2016-09, Vol.18 (9), p.1783-1795 |
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| container_title | IEEE transactions on multimedia |
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| creator | Jianjun Lei Bingren Wang Yuming Fang Weisi Lin Le Callet, Patrick Nam Ling Chunping Hou |
| description | In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. First, rough salient regions are extracted from any existing saliency detection model with distance weighting, adaptive binarization, and morphological closing. With the superpixel segmentation, a Bayesian decision model is adopted to refine the rough saliency map to obtain a more accurate saliency map. An iterative optimization method is designed to obtain better saliency results by exploiting the characteristics of the output saliency map each time. Through the iterative optimization process, the rough saliency map is updated step by step with better and better performance until an optimal saliency map is obtained. Experimental results on the public salient object detection datasets with ground truth demonstrate the promising performance of the proposed universal framework subjectively and objectively. |
| doi_str_mv | 10.1109/TMM.2016.2592325 |
| format | Article |
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(IEEE) 2016</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-d45de58050288ea202429f79ac376fee9676dc306c2580449eb68ada8e8925863</citedby><cites>FETCH-LOGICAL-c358t-d45de58050288ea202429f79ac376fee9676dc306c2580449eb68ada8e8925863</cites><orcidid>0000-0002-2143-7063 ; 0000-0003-3171-7680</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7514760$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7514760$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-01675158$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Jianjun Lei</creatorcontrib><creatorcontrib>Bingren Wang</creatorcontrib><creatorcontrib>Yuming Fang</creatorcontrib><creatorcontrib>Weisi Lin</creatorcontrib><creatorcontrib>Le Callet, Patrick</creatorcontrib><creatorcontrib>Nam Ling</creatorcontrib><creatorcontrib>Chunping Hou</creatorcontrib><title>A Universal Framework for Salient Object Detection</title><title>IEEE transactions on multimedia</title><addtitle>TMM</addtitle><description>In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. 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Experimental results on the public salient object detection datasets with ground truth demonstrate the promising performance of the proposed universal framework subjectively and objectively.</description><subject>Adaptation models</subject><subject>Bayesian analysis</subject><subject>Computational modeling</subject><subject>Computer Science</subject><subject>Feature extraction</subject><subject>Ground truth</subject><subject>Image color analysis</subject><subject>Image Processing</subject><subject>Iterative methods</subject><subject>Iterative optimization</subject><subject>Multimedia</subject><subject>Object detection</subject><subject>Object recognition</subject><subject>Optimization</subject><subject>salient object detection</subject><subject>Segmentation</subject><subject>universal framework</subject><subject>visual attention</subject><subject>Visualization</subject><subject>Weighting</subject><issn>1520-9210</issn><issn>1941-0077</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpd0EFPwjAUB_DGaCKidxMvS7zoYfjarl17JChiAuEgnJuyvcXhWLEdGL-9JRAPnl7z8nvNe39CbikMKAX9tJjNBgyoHDChGWfijPSozmgKkOfn8S0YpJpRuCRXIawBaCYg7xE2TJZtvUcfbJOMvd3gt_OfSeV88m6bGtsuma_WWHTJM3ax1K69JheVbQLenGqfLMcvi9Eknc5f30bDaVpwobq0zESJQoEAphRaBixjusq1LXguK0Qtc1kWHGTBosoyjSupbGkVKh07kvfJ4_HfD9uYra831v8YZ2szGU7NoRevzQUVak-jfTjarXdfOwyd2dShwKaxLbpdMFRxIUEB1ZHe_6Nrt_NtvCQqSrmgXLGo4KgK70LwWP1tQMEcAjcxcHMI3JwCjyN3x5EaEf943DDLJfBfZzx4AQ</recordid><startdate>20160901</startdate><enddate>20160901</enddate><creator>Jianjun Lei</creator><creator>Bingren Wang</creator><creator>Yuming Fang</creator><creator>Weisi Lin</creator><creator>Le Callet, Patrick</creator><creator>Nam Ling</creator><creator>Chunping Hou</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptation models Bayesian analysis Computational modeling Computer Science Feature extraction Ground truth Image color analysis Image Processing Iterative methods Iterative optimization Multimedia Object detection Object recognition Optimization salient object detection Segmentation universal framework visual attention Visualization Weighting |
| title | A Universal Framework for Salient Object Detection |
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