Object Level Image Saliency By Hierarchical Segmentation

Conventional saliency detection approaches are human fixation detection and single dom-inant region detection. However, real-world photographs usually consist of multiple dominant regions. We propose a saliency detection method with the aim to highlight the whole object and distinguish objects with different saliency levels. It combines the bottom-up approach and top-down approach via two nested levels of hierarchical segmentations-the coarse level objects and fine level details. We first calculate a preliminary saliency on the fine patches with a random walk model. Then two mid-level cues and one object-level cue are fused to refine the preliminary saliency to emphasize the objects against the background. At last, the object-level saliency map is synthesized via a heat diffusion process restricted by the coarse level patches to enhance object saliency and distinguish saliency between different objects. Extensive evaluation on a publicly available database verifies that our method outperforms the state-of-the-art algorithms.