Saliency Detection via Bidirectional Absorbing Markov Chain

Citation

Jiang F, Kong B, Adeel A, Xiao Y & Hussain A (2018) Saliency Detection via Bidirectional Absorbing Markov Chain. In: Ren J, Hussain A, Zheng J, Liu C, Luo B, Zhao H & Zhao X (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science, 10989. BICS 2018: International Conference on Brain Inspired Cognitive Systems, 07.07.2018-08.07.2018. Cham, Switzerland: Springer Verlag, pp. 495-505. https://doi.org/10.1007/978-3-030-00563-4_48

Abstract
Traditional saliency detection via Markov chain only consider boundaries nodes. However, in addition to boundaries cues, background prior and foreground prior cues play a complementary role to enhance saliency detection. In this paper, we propose an absorbing Markov chain based saliency detection method considering both boundary information and foreground prior cues. The proposed approach combines both boundaries and foreground prior cues through bidirectional Markov chain. Specifically, the image is first segmented into superpixels and four boundaries nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node’s random walk to the absorbing state is calculated to obtain foreground possibility. Simultaneously, foreground prior as the virtual absorbing nodes is used to calculate the absorption time and obtain the background possibility. Finally, two obtained results are fused to obtain the combined saliency map using cost function for further optimization at multi-scale. Experimental results demonstrate the outperformance of our proposed model on 4 benchmark datasets as compared to 17 state-of-the-art methods.

Keywords
Saliency detection; Markov chain; Bidirectional absorbing