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Article: A New Probabilistic Representation of Color Image Pixels and Its Applications

TitleA New Probabilistic Representation of Color Image Pixels and Its Applications
Authors
KeywordsProbabilistic color representation
pixel-wise similarity
region-wise similarity
image matching
registration
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83
Citation
IEEE Transactions on Image Processing, 2019, v. 28 n. 4, p. 2037-2050 How to Cite?
AbstractThis paper proposes a novel probabilistic representation of color image (PRCI) pixels and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them, respectively, in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that: 1) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem and 2) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.
Persistent Identifierhttp://hdl.handle.net/10722/294064
ISSN
2019 Impact Factor: 9.34
2015 SCImago Journal Rankings: 2.727

 

DC FieldValueLanguage
dc.contributor.authorLIN, Z-
dc.contributor.authorQIN, H-
dc.contributor.authorChan, SC-
dc.date.accessioned2020-11-23T08:25:48Z-
dc.date.available2020-11-23T08:25:48Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Image Processing, 2019, v. 28 n. 4, p. 2037-2050-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/294064-
dc.description.abstractThis paper proposes a novel probabilistic representation of color image (PRCI) pixels and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them, respectively, in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that: 1) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem and 2) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.rightsIEEE Transactions on Image Processing. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectProbabilistic color representation-
dc.subjectpixel-wise similarity-
dc.subjectregion-wise similarity-
dc.subjectimage matching-
dc.subjectregistration-
dc.titleA New Probabilistic Representation of Color Image Pixels and Its Applications-
dc.typeArticle-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2018.2883580-
dc.identifier.scopuseid_2-s2.0-85058078380-
dc.identifier.hkuros319260-
dc.identifier.volume28-
dc.identifier.issue4-
dc.identifier.spage2037-
dc.identifier.epage2050-
dc.publisher.placeUnited States-

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