Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 12, Issue 4, Pages 300-316Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2010.2047607
Keywords
Bayesian framework; bottom-up; observation behavior; salient object detection; top-down
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Funding
- Natural Sciences and Engineering Research Council of Canada
- NSFC [60802025]
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Salient object detection is a basic technique for many computer vision applications. In this paper, we propose an adaptive computational model to detect the salient object in color images. Firstly, three human observation behaviors and scalable subtractive clustering techniques are used to construct attention Gaussian mixture model (AGMM) and background Gaussian mixture model (BGMM). Secondly, the Bayesian framework is employed to classify each pixel into salient object or background object. Thirdly, expectation-maximization (EM) algorithm is utilized to update the parameters of AGMM, BGMM, and Bayesian framework based on the detection results. Finally, the classification and update procedures are repeated until the detection results evolve to a steady state. Experiments on a variety of images demonstrate the robustness of the proposed method. Extensive quantitative evaluations and comparisons demonstrate that the proposed method significantly outperforms state-of-the-art methods.
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