Saliency Detection via Absorbing Markov Chain with Multi-level Cues

Abstract

A robust saliency detection approach for images with a complex background is proposed. The absorbing Markov chain integrating low-level, mid-level and high-level cues dynamically evolves by using the similarity between pixels to detect saliency objects. The experimental results show that the proposed algorithm has advantages in saliency detection, especially for images with a chaotic background or low contrast.