Saliency detection via global-object-seed-guided cellular automata
Image saliency detection has attracted much attention in recent years, while several challenging problems are still unsolved, such as inaccurate saliency detection in complex scenes and suppressing salient objects near image borders. In this paper, a novel algorithm is proposed to solve these problems. Firstly, we collect background seeds from image borders by boundary information and construct a background-based saliency map via low level features. Then, a novel propagation mechanism named global-object-seed-guided Cellular Automata model is builded. Cellular Automata exploits the intrinsic relevance of similar regions through interactions with neighbors, and global object seeds reduce the difference between dissimilar adjacent regions in the whole salient object. Experimental results on public benchmark datasets demonstrate the superiority of the proposed algorithm over ten state-of-the-art saliency models.