Salient Object Detection and Segmentation via Ultra-Contrast

Salient object detection aims at finding the most conspicuous objects in an image that highly catches the user’s attention. The traditional contrast based salient object detection algorithms focus on highlighting the most dissimilar regions and generally fail to detect complex salient objects. In this paper, we propose a salient object detection principle from existing contrast based methods:
dissimilarity produces contrast, while contrast leads to saliency
. Guided by this principle, we propose a generalized framework to detect complex salient objects. First, we propose a set of region dissimilarity definitions inspired by diverse saliency cues. Then, multiple contrast contexts are encoded to derive dissimilarity matrices. Afterwards, multiple contrast transformations are designed to convert dissimilarity matrices into unified ultra-contrast features. Finally, these ultra-contrast features are mapped to saliency values through logistic regression. The proposed framework is capable of flexibly integrating different kinds of region dissimilarity definitions, region contexts, and contrast transformations. The experimental results demonstrate that our ultra-contrast based saliency detection method outperforms existing contrast based algorithms in terms of three metrics on four datasets.