Salient Region Detection with Hierarchical Image Abstraction

Salient region detection is important for many high-level computer vision tasks. The majority of previous works exploit element contrast to detect image saliency region. In this paper, we propose a novel approach to analyze saliency cues from multiple scales of image structure, using a multi-scale image abstraction. In each image layer global color contrast cue and color spatial distribution cue are integrated to generate a single-layer saliency map, and then the final saliency map can be obtained by across-scale adding several single-layer saliency maps. The proposed saliency estimation method abstracts unnecessary image detail, obtaining high quality saliency detection results. We have evaluated the results of our method on the two publicly available datasets MSRA-1000 and SED. The experimental results on these datasets demonstrate the effectiveness of the approaches against the other approaches to analyze image saliency.