  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.
