Saliency Detection for Stereoscopic Images
Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection...
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| Veröffentlicht in: | IEEE transactions on image processing 2014-06, Vol.23 (6), p.2625-2636 |
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| container_title | IEEE transactions on image processing |
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| creator | Yuming Fang Junle Wang Narwaria, Manish Le Callet, Patrick Weisi Lin |
| description | Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. In addition, we adopt the center bias factor and human visual acuity, the important characteristics of the human visual system, to enhance the final saliency map for stereoscopic images. Experimental results on eye tracking databases show the superior performance of the proposed model over other existing methods. |
| doi_str_mv | 10.1109/TIP.2014.2305100 |
| format | Article |
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Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. In addition, we adopt the center bias factor and human visual acuity, the important characteristics of the human visual system, to enhance the final saliency map for stereoscopic images. Experimental results on eye tracking databases show the superior performance of the proposed model over other existing methods.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2014.2305100</identifier><identifier>PMID: 24832595</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>3D image ; Algorithms ; Applied sciences ; Artificial Intelligence ; Computational modeling ; Computer Science ; Detection, estimation, filtering, equalization, prediction ; Engineering Sciences ; Exact sciences and technology ; Feature extraction ; Human ; human visual acuity ; Image color analysis ; Image detection ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Imaging, Three-Dimensional - methods ; Information, signal and communications theory ; Luminance ; Mathematical models ; Pattern Recognition, Automated - methods ; Photogrammetry - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal and communications theory ; Signal and Image Processing ; Signal processing ; Signal, noise ; Solid modeling ; Stereo image processing ; Stereoscopic ; Stereoscopic image ; stereoscopic saliency detection ; Subtraction Technique ; Surface layer ; Telecommunications and information theory ; Texture ; Three-dimensional displays ; Two dimensional ; visual attention ; Visualization</subject><ispartof>IEEE transactions on image processing, 2014-06, Vol.23 (6), p.2625-2636</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2014</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c486t-c18ba3487be52f808d87170568af2a4508629455ffb2031b0a95032ffcfc854a3</citedby><cites>FETCH-LOGICAL-c486t-c18ba3487be52f808d87170568af2a4508629455ffb2031b0a95032ffcfc854a3</cites><orcidid>0000-0002-2143-7063 ; 0000-0002-6946-3586</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6733288$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6733288$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28528611$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24832595$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-01059986$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Yuming Fang</creatorcontrib><creatorcontrib>Junle Wang</creatorcontrib><creatorcontrib>Narwaria, Manish</creatorcontrib><creatorcontrib>Le Callet, Patrick</creatorcontrib><creatorcontrib>Weisi Lin</creatorcontrib><title>Saliency Detection for Stereoscopic Images</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. In addition, we adopt the center bias factor and human visual acuity, the important characteristics of the human visual system, to enhance the final saliency map for stereoscopic images. Experimental results on eye tracking databases show the superior performance of the proposed model over other existing methods.</description><subject>3D image</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Computational modeling</subject><subject>Computer Science</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Engineering Sciences</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Human</subject><subject>human visual acuity</subject><subject>Image color analysis</subject><subject>Image detection</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information, signal and communications theory</subject><subject>Luminance</subject><subject>Mathematical models</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Photogrammetry - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Signal and communications theory</subject><subject>Signal and Image Processing</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Solid modeling</subject><subject>Stereo image processing</subject><subject>Stereoscopic</subject><subject>Stereoscopic image</subject><subject>stereoscopic saliency detection</subject><subject>Subtraction Technique</subject><subject>Surface layer</subject><subject>Telecommunications and information theory</subject><subject>Texture</subject><subject>Three-dimensional displays</subject><subject>Two dimensional</subject><subject>visual attention</subject><subject>Visualization</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0d9LHDEQB_BQlPqjvhcKclCEKuw5-THZ5FFsqwcHFe58DtmY2JW9zTXZE_zvzXHXE3zxKSH5zDDDl5CvFMaUgr6cT-7GDKgYMw5IAT6RQ6oFrQAE2yt3wLqqqdAH5CjnJygSqfxMDphQnKHGQ3Ixs13re_cy-ukH74Y29qMQ02g2-ORjdnHZutFkYR99_kL2g-2yP9mex-T-96_59W01_XMzub6aVk4oOVSOqsZyoerGIwsK1IOqaQ0olQ3MCgQlmRaIITQMOG3AagTOQnDBKRSWH5PzTd-_tjPL1C5sejHRtub2amrWb1D20lrJZ1rsj41dpvhv5fNgFm12vuts7-MqGyql1kWD_pgiw1qyWrFCv7-jT3GV-rJ0UQK1FChFUbBRLsWckw-7YSmYdTymxGPW8ZhtPKXkdNt41Sz8w67gfx4FnG2Bzc52IdnetfnNKWRK0vXe3zau9d7vvmXNOVOKvwI5vJuC</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Yuming Fang</creator><creator>Junle Wang</creator><creator>Narwaria, Manish</creator><creator>Le Callet, Patrick</creator><creator>Weisi Lin</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yuming Fang</au><au>Junle Wang</au><au>Narwaria, Manish</au><au>Le Callet, Patrick</au><au>Weisi Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Saliency Detection for Stereoscopic Images</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2014-06-01</date><risdate>2014</risdate><volume>23</volume><issue>6</issue><spage>2625</spage><epage>2636</epage><pages>2625-2636</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. 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| subjects | 3D image Algorithms Applied sciences Artificial Intelligence Computational modeling Computer Science Detection, estimation, filtering, equalization, prediction Engineering Sciences Exact sciences and technology Feature extraction Human human visual acuity Image color analysis Image detection Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Imaging, Three-Dimensional - methods Information, signal and communications theory Luminance Mathematical models Pattern Recognition, Automated - methods Photogrammetry - methods Reproducibility of Results Sensitivity and Specificity Signal and communications theory Signal and Image Processing Signal processing Signal, noise Solid modeling Stereo image processing Stereoscopic Stereoscopic image stereoscopic saliency detection Subtraction Technique Surface layer Telecommunications and information theory Texture Three-dimensional displays Two dimensional visual attention Visualization |
| title | Saliency Detection for Stereoscopic Images |
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