Open AccessSalient object detection based on global multi‐scale superpixel contrast
Author(s) -
Yang Jinfu,
Wang Ying,
Wang Guanghui,
Li Mingai
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0469
Subject(s) - contrast (vision) , artificial intelligence , salient , computer science , pattern recognition (psychology) , object detection , scale (ratio) , object (grammar) , computer vision , image (mathematics) , geography , cartography
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.