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An Adaptive Computational Model for Salient Object Detection
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DOI：10.1109/TMM.2010.2047607.png)
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Abstract
En 中文
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.
Keywords:
Bayesian framework
bottom-up
observation behavior
salient object detection
top-down
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