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Indexed by:期刊论文

Date of Publication:2014-09-01

Journal:IEEE SIGNAL PROCESSING LETTERS

Included Journals:SCIE、EI、Scopus

Volume:21

Issue:9

Page Number:1035-1039

ISSN No.:1070-9908

Key Words:Multi-scale analysis; saliency map; visual saliency

Abstract:We propose a salient object detection algorithm via multi-scale analysis on superpixels. First, multi-scale segmentations of an input image are computed and represented by superpixels. In contrast to prior work, we utilize various Gaussian smoothing parameters to generate coarse or fine results, thereby facilitating the analysis of salient regions. At each scale, three essential cues from local contrast, integrity and center bias are considered within the Bayesian framework. Next, we compute saliency maps by weighted summation and normalization. The final saliency map is optimized by a guided filter which further improves the detection results. Extensive experiments on two large benchmark datasets demonstrate the proposed algorithm performs favorably against state-of-the-art methods. The proposed method achieves the highest precision value of 97.39% when evaluated on one of the most popular datasets, the ASD dataset.