Salient object detection based on global multi‐scale superpixel contrast. Issue 8 (25th September 2017)

• Record Type: Journal Article
• Authors: Yang, Jinfu; Wang, Ying; Wang, Guanghui; Li, Mingai
• Is Part Of: IET computer vision. Volume 11:Issue 8(2017)
• Page Start: 710
• Page End: 716
• Publication Date: 2017-09-25
• Subjects: image resolution -- computer vision -- object detection -- cellular automata; global multiscale superpixel contrast -- computer vision -- novel salient object detection method -- high-level information -- modified multilayer cellular automata; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0469
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Salient object detection, as a necessary step of many computer vision applications, has attracted extensive attention in recent years. A novel salient object detection method is proposed based on multi‐superpixel‐scale contrast. Saliency value of each superpixel is measured with a global score, which is computed using the region's colour contrast and the spatial distances to all other regions in the image. High‐level information is also incorporated to improve the performance, and the saliency maps are fused across multiple levels to yield a reliable final result using the modified multi‐layer cellular automata. The proposed algorithm is evaluated and compared with five state‐of‐the‐art approaches on three publicly standard datasets. Both quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of the proposed method.