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A global and local consistent ranking model for image saliency computation.

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Title: A global and local consistent ranking model for image saliency computation.
Authors: Xiao, Yun1, Wang, Liangmin2, Jiang, Bo1, Tu, Zhengzheng1, Tang, Jin1 ahu_tj@163.com
Source: Journal of Visual Communication & Image Representation. Jul2017, Vol. 46, p199-207. 9p.
Subjects: Image processing, Statistical methods in image analysis, Markov processes, Manifolds (Mathematics), Image databases
Abstract: Image saliency detection is an important issue in computer vision and has been widely used in many applications. In this paper, we propose a new global and local consistent ranking (GLR) model for image saliency computation. Firstly, we propose to use an absorbed Markov chain model to obtain a kind of global ranking for image superpixels, in which the absorbing nodes represent the virtual boundary superpixels and the transient nodes denote the general superpixels of image. Then, the absorbed time from each transient node to boundary absorbing nodes are computed. This absorbing time of transient node measures its global similarity with all absorbing nodes and thus provides a kind of global ranking for each transient node w.r.t. absorbing nodes. At last, we further exploit the local manifold structure and incorporate the local manifold smooth information into ranking process and thus propose a general global and local consistent ranking for saliency detection. Experimental results on several large benchmark databases show the effectiveness of the proposed GLR method. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Visual Communication & Image Representation is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 123342778
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A global and local consistent ranking model for image saliency computation.
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  Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Yun%22">Xiao, Yun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Liangmin%22">Wang, Liangmin</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Bo%22">Jiang, Bo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tu%2C+Zhengzheng%22">Tu, Zhengzheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tang%2C+Jin%22">Tang, Jin</searchLink><relatesTo>1</relatesTo><i> ahu_tj@163.com</i>
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  Data: <searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+methods+in+image+analysis%22">Statistical methods in image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Manifolds+%28Mathematics%29%22">Manifolds (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+databases%22">Image databases</searchLink>
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  Data: Image saliency detection is an important issue in computer vision and has been widely used in many applications. In this paper, we propose a new global and local consistent ranking (GLR) model for image saliency computation. Firstly, we propose to use an absorbed Markov chain model to obtain a kind of global ranking for image superpixels, in which the absorbing nodes represent the virtual boundary superpixels and the transient nodes denote the general superpixels of image. Then, the absorbed time from each transient node to boundary absorbing nodes are computed. This absorbing time of transient node measures its global similarity with all absorbing nodes and thus provides a kind of global ranking for each transient node w.r.t. absorbing nodes. At last, we further exploit the local manifold structure and incorporate the local manifold smooth information into ranking process and thus propose a general global and local consistent ranking for saliency detection. Experimental results on several large benchmark databases show the effectiveness of the proposed GLR method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Visual Communication & Image Representation is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=123342778
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        Value: 10.1016/j.jvcir.2017.04.001
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 199
    Subjects:
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Statistical methods in image analysis
        Type: general
      – SubjectFull: Markov processes
        Type: general
      – SubjectFull: Manifolds (Mathematics)
        Type: general
      – SubjectFull: Image databases
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      – TitleFull: A global and local consistent ranking model for image saliency computation.
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            NameFull: Xiao, Yun
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            NameFull: Wang, Liangmin
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            NameFull: Jiang, Bo
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            NameFull: Tu, Zhengzheng
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            NameFull: Tang, Jin
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            – D: 01
              M: 07
              Text: Jul2017
              Type: published
              Y: 2017
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              Value: 46
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