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.
Achanta @cite_21 propose a frequency-tuned method that defines pixel saliency using color differences from the average image color and a Gaussian blurred version of the image.
@cite_21
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We proposed a computational visual saliency modeling technique. The proposed technique makes use of a color co-occurrence histogram (CCH) that captures not only "how many" but also "where and how" image pixels are composed into a visually perceivable image. Hence the CCH encodes image saliency information that is usually perceived as the discontinuity between an image region or object and its surrounding. The proposed technique has a number of distinctive characteristics: It is fast, discriminative, tolerant to image scale variation, and involves minimal parameter tuning. Experiments over benchmarking datasets show that it predicts fixational eye tracking points accurately and a superior AUC of 71.25 is obtained.