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

Mian Muhammad Sadiq Fareed, Qi Chun*, Gulnaz Ahmed, Muhammad Rizwan Asif, Muhammad Zeeshan Fareed

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

6 Citations (Scopus)

Abstract

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
Volume70
Pages (from-to)551-566
Number of pages16
ISSN0045-7906
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

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

Fingerprint

Dive into the research topics of 'Saliency detection by exploiting multi-features of color contrast and color distribution'. Together they form a unique fingerprint.

Cite this