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Muhammad Rizwan Asif

Saliency detection by exploiting multi-features of color contrast and color distribution

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  • Mian Muhammad Sadiq Fareed, Xi'an Jiaotong University
  • ,
  • Qi Chun, Xi'an Jiaotong University
  • ,
  • Gulnaz Ahmed, Xi'an Jiaotong University
  • ,
  • Muhammad Rizwan Asif
  • Muhammad Zeeshan Fareed, Xi'an Jiaotong University

Automatic salient object detection from a cluttered image using the object prior information related to the image enhances the accuracy of object detection which is very useful for many computer vision applications. In this work, we introduce a new bottom-up approach for salient object detection by incorporating the multi-features of color contrast with background connectivity weight and color distribution. Firstly, we extract coarse saliency map by using a color contrast with background connectivity weight and the color distribution. Secondly, we improve the coarse saliency map result through a multi-features global optimization energy function. This energy function is used to fuse several low-level measures, to evenly highlight the salient object and suppress the background efficiently. Extensive experiments on the benchmark datasets have been performed to demonstrate that the proposed model outperforms against the existed state-of-the-art methods with the higher values of precision and recall.

Original languageEnglish
JournalComputers and Electrical Engineering
Pages (from-to)551-566
Number of pages16
Publication statusPublished - Aug 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (Grant no. 61572395 ), in part by the National High-tech Research and Development Program of China (Grant no. 2009AA01Z321) and in part by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant no. 20110201110012).

Publisher Copyright:
© 2017

    Research areas

  • Color contrast, Color distribution, Energy cost function, Location prior, Saliency map

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