Saliency Detection via Bidirectional Absorbing Markov Chain

TL;DR

This paper introduces a bidirectional absorbing Markov chain method for saliency detection that integrates boundary, background, and foreground cues, leading to improved accuracy over existing methods.

Contribution

It proposes a novel bidirectional Markov chain approach that combines boundary and foreground prior cues for enhanced saliency detection.

Findings

Outperforms 17 state-of-the-art methods on 4 benchmark datasets.

Effectively integrates boundary and foreground cues for better saliency maps.

Demonstrates robustness across different image datasets.

Abstract

Traditional saliency detection via Markov chain only considers 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.