In this paper we propose two saliency models for salient object segmentation based on a hierarchical image segmentation, a tree-like structure that represents regions at different scales from the details to the whole image (e.g. gPb-UCM, BPT). The first model is based on a hierarchy of image partitions. The saliency at each level is computed on a region basis, taking into account the contrast between regions. The maps obtained for the different partitions are then integrated into a final saliency map. The second model directly works on the structure created by the segmentation algorithm, computing saliency at each node and integrating these cues in a straightforward manner into a single saliency map. We show that the proposed models produce high quality saliency maps. Objective evaluation demonstrates that the two methods achieve state-of-the-art performance in several benchmark datasets. HighlightsModel for salient object segmentation based on hierarchies of image partitions.High quality saliency maps.Detection of salient objects of different scales with accurate boundaries.Performance comparable to state-of-the-art techniques in terms of precision-recall.
@cite_14: When dealing with objects with complex structures, saliency detection confronts a critical problem - namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed.
Yan @cite_14 use a hierarchy of three levels. They first generate an over-segmentation of the image by a watershed-like method and then apply an iterative merging process based on the scale and the color of the regions. Saliency maps are created for the three levels and merged into a final map by hierarchical inference. This inference is equivalent to applying a weighted average to all single saliency maps with optimal weights for each region.