Adaptive semantic Bayesian framework for image attention

Image attention is the basic technique for many computer vision applications. In this paper, we propose an adaptive Bayesian framework to detect the image attention in color image. Firstly, three simple semantics and subtractive clustering are used to construct attention Gaussians mixture model (AGMM) and background Gaussians mixture model (BGMM). Secondly, the Bayesian framework is utilized to classify each pixel into attention objects and background objects. Thirdly, EM algorithm is used to update the parameters of AGMM, BGMM, and Bayesian framework according to the detection results. Finally, the above classification and update procedures are repeated until the detection results become steady. Experimental results on typical images exhibit the robustness of the proposed method.