This paper introduces a geometric aesthetic approach for the analysis of visual attention to extract regions of interest from images. Modulation awareness, such as that perceived by visual features, can be represented by attractive proportions of visual objects. Together with supporting techniques such as similarity estimation and lighting condition manipulation, the aesthetic geometry-based analysis can be implemented to form refined attentive shifting observed from image scenes. In this paper, we propose robust kernels which comply with the golden ratio for analysis of aesthetic attractiveness which can raise visual awareness. Properties and relations of points and regions are evaluated by the corresponding kernels for images scenes. We also establish robust likelihood reasoning for the kernels with respect to human aesthetic attraction. The experimental results with a benchmark show the efficiency of the proposed method for identifying region of visual interest in images.
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