Saliency detection by exploiting multi-features of color contrast and color distribution. (August 2018)

• Record Type: Journal Article
• Authors: Sadiq Fareed, Mian Muhammad; Chun, Qi; Ahmed, Gulnaz; Asif, Muhammad Rizwan; Fareed, Muhammad Zeeshan
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 551
• Page End: 566
• Publication Date: 2018-08
• Subjects: Color contrast -- Color distribution -- Location prior -- Energy cost function -- Saliency map; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.027
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We employed background connectivity with outer boundary for saliency detection. We used several low level measures to accurately detect the salient object. We improve initial saliency map results through a multi-features optimization function. Refinement procedure repairs the lost information and helps in suppressing background. Graphical abstract: 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.