Loading…
Efficient Bayesian approach to saliency detection based on Dirichlet process mixture
Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of...
Saved in:
Published in: | IET image processing 2017-11, Vol.11 (11), p.1103-1113 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673 |
---|---|
cites | cdi_FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673 |
container_end_page | 1113 |
container_issue | 11 |
container_start_page | 1103 |
container_title | IET image processing |
container_volume | 11 |
creator | Rabbani, Navid Nazari, Behzad Sadri, Saeid Rikhtehgaran, Reyhaneh |
description | Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre-surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state-of-the-art methods. Also, they show that the proposed method has a low computational cost. |
doi_str_mv | 10.1049/iet-ipr.2017.0267 |
format | article |
fullrecord | <record><control><sourceid>wiley_24P</sourceid><recordid>TN_cdi_crossref_primary_10_1049_iet_ipr_2017_0267</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>IPR2BF01504</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673</originalsourceid><addsrcrecordid>eNqFkFFLwzAQx4MoOKcfwLd8gc5LmqSrb246HQwUmc8hTa8so2tL0qH99qZMfNSn-8Pd7477EXLLYMZA5HcO-8R1fsaBZTPgKjsjE5ZJluRKZee_WeaX5CqEPYDMYS4nZPtUVc46bHq6MAMGZxpqus63xu5o39Jg6ti0Ay2xR9u7tqGFCVjSGB6dd3ZXY0_jvMUQ6MF99UeP1-SiMnXAm586JR-rp-3yJdm8Pq-XD5vECgCRZKUpAK1IORNWcCMzhVwWpRQgFQcslYLUmLIwohRSscIyOcfUpiY3qlJZOiXstNf6NgSPle68Oxg_aAZ61KKjFh216FGLHrVE5v7EfLoah_8BvX5754sVMAkiwskJHsf27dE38b0_jn0Dmq15qQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Efficient Bayesian approach to saliency detection based on Dirichlet process mixture</title><source>Wiley-Blackwell Open Access Collection</source><creator>Rabbani, Navid ; Nazari, Behzad ; Sadri, Saeid ; Rikhtehgaran, Reyhaneh</creator><creatorcontrib>Rabbani, Navid ; Nazari, Behzad ; Sadri, Saeid ; Rikhtehgaran, Reyhaneh</creatorcontrib><description>Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre-surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state-of-the-art methods. Also, they show that the proposed method has a low computational cost.</description><identifier>ISSN: 1751-9659</identifier><identifier>ISSN: 1751-9667</identifier><identifier>EISSN: 1751-9667</identifier><identifier>DOI: 10.1049/iet-ipr.2017.0267</identifier><language>eng</language><publisher>The Institution of Engineering and Technology</publisher><subject>Bayes methods ; centre‐surround saliency ; Dirichlet process mixture model ; efficient Bayesian approach ; feature‐based saliency ; image processing ; image processing application ; image saliency ; location‐based saliency ; Research Article ; saliency detection ; statistical analysis ; statistical approach</subject><ispartof>IET image processing, 2017-11, Vol.11 (11), p.1103-1113</ispartof><rights>The Institution of Engineering and Technology</rights><rights>2021 The Authors. IET Image Processing published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673</citedby><cites>FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1049%2Fiet-ipr.2017.0267$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1049%2Fiet-ipr.2017.0267$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,9755,11562,27924,27925,46052,46476</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-ipr.2017.0267$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc></links><search><creatorcontrib>Rabbani, Navid</creatorcontrib><creatorcontrib>Nazari, Behzad</creatorcontrib><creatorcontrib>Sadri, Saeid</creatorcontrib><creatorcontrib>Rikhtehgaran, Reyhaneh</creatorcontrib><title>Efficient Bayesian approach to saliency detection based on Dirichlet process mixture</title><title>IET image processing</title><description>Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre-surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state-of-the-art methods. Also, they show that the proposed method has a low computational cost.</description><subject>Bayes methods</subject><subject>centre‐surround saliency</subject><subject>Dirichlet process mixture model</subject><subject>efficient Bayesian approach</subject><subject>feature‐based saliency</subject><subject>image processing</subject><subject>image processing application</subject><subject>image saliency</subject><subject>location‐based saliency</subject><subject>Research Article</subject><subject>saliency detection</subject><subject>statistical analysis</subject><subject>statistical approach</subject><issn>1751-9659</issn><issn>1751-9667</issn><issn>1751-9667</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqFkFFLwzAQx4MoOKcfwLd8gc5LmqSrb246HQwUmc8hTa8so2tL0qH99qZMfNSn-8Pd7477EXLLYMZA5HcO-8R1fsaBZTPgKjsjE5ZJluRKZee_WeaX5CqEPYDMYS4nZPtUVc46bHq6MAMGZxpqus63xu5o39Jg6ti0Ay2xR9u7tqGFCVjSGB6dd3ZXY0_jvMUQ6MF99UeP1-SiMnXAm586JR-rp-3yJdm8Pq-XD5vECgCRZKUpAK1IORNWcCMzhVwWpRQgFQcslYLUmLIwohRSscIyOcfUpiY3qlJZOiXstNf6NgSPle68Oxg_aAZ61KKjFh216FGLHrVE5v7EfLoah_8BvX5754sVMAkiwskJHsf27dE38b0_jn0Dmq15qQ</recordid><startdate>201711</startdate><enddate>201711</enddate><creator>Rabbani, Navid</creator><creator>Nazari, Behzad</creator><creator>Sadri, Saeid</creator><creator>Rikhtehgaran, Reyhaneh</creator><general>The Institution of Engineering and Technology</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201711</creationdate><title>Efficient Bayesian approach to saliency detection based on Dirichlet process mixture</title><author>Rabbani, Navid ; Nazari, Behzad ; Sadri, Saeid ; Rikhtehgaran, Reyhaneh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Bayes methods</topic><topic>centre‐surround saliency</topic><topic>Dirichlet process mixture model</topic><topic>efficient Bayesian approach</topic><topic>feature‐based saliency</topic><topic>image processing</topic><topic>image processing application</topic><topic>image saliency</topic><topic>location‐based saliency</topic><topic>Research Article</topic><topic>saliency detection</topic><topic>statistical analysis</topic><topic>statistical approach</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rabbani, Navid</creatorcontrib><creatorcontrib>Nazari, Behzad</creatorcontrib><creatorcontrib>Sadri, Saeid</creatorcontrib><creatorcontrib>Rikhtehgaran, Reyhaneh</creatorcontrib><collection>CrossRef</collection><jtitle>IET image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rabbani, Navid</au><au>Nazari, Behzad</au><au>Sadri, Saeid</au><au>Rikhtehgaran, Reyhaneh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient Bayesian approach to saliency detection based on Dirichlet process mixture</atitle><jtitle>IET image processing</jtitle><date>2017-11</date><risdate>2017</risdate><volume>11</volume><issue>11</issue><spage>1103</spage><epage>1113</epage><pages>1103-1113</pages><issn>1751-9659</issn><issn>1751-9667</issn><eissn>1751-9667</eissn><abstract>Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location-based, feature-based and centre-surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre-surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state-of-the-art methods. Also, they show that the proposed method has a low computational cost.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/iet-ipr.2017.0267</doi><tpages>11</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1751-9659 |
ispartof | IET image processing, 2017-11, Vol.11 (11), p.1103-1113 |
issn | 1751-9659 1751-9667 1751-9667 |
language | eng |
recordid | cdi_crossref_primary_10_1049_iet_ipr_2017_0267 |
source | Wiley-Blackwell Open Access Collection |
subjects | Bayes methods centre‐surround saliency Dirichlet process mixture model efficient Bayesian approach feature‐based saliency image processing image processing application image saliency location‐based saliency Research Article saliency detection statistical analysis statistical approach |
title | Efficient Bayesian approach to saliency detection based on Dirichlet process mixture |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T20%3A53%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-wiley_24P&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Efficient%20Bayesian%20approach%20to%20saliency%20detection%20based%20on%20Dirichlet%20process%20mixture&rft.jtitle=IET%20image%20processing&rft.au=Rabbani,%20Navid&rft.date=2017-11&rft.volume=11&rft.issue=11&rft.spage=1103&rft.epage=1113&rft.pages=1103-1113&rft.issn=1751-9659&rft.eissn=1751-9667&rft_id=info:doi/10.1049/iet-ipr.2017.0267&rft_dat=%3Cwiley_24P%3EIPR2BF01504%3C/wiley_24P%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c4004-7dab0ec43214c42a576e25bd5405620ed6603aadba4d4561bc158e3c3a9a6f673%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |