Salient Region Detection based on Object-Biased Gaussian Refinement and Global Contrast

Abstract

Abstract: In view of the disadvantages such as the incomplete detection in image boundary which is caused by the global contrast significance detection algorithm, a visual saliency detection algorithm was proposed named salient region detection based on object-biased Gaussian refinement and global contrast. After segmentation based on graph, the global contrast was used to calculate the saliency value of each segmentation blocks. According to the location of the salient object, the center of the Gaussian model was adjusted. Both saliency detection value and Gaussian model combined effect of the final saliency values. The method paid attention to the global contrast and the spatial position of a salient object and improving the way to determine the image center. Theoretical analysis and experiments demonstrate that the method has a better saliency result which can detect the salient object region of all kinds of image effectively. The subjective quality is improved obviously, and the objective indicators are improved partly.

Key words: object-biased, global contrast, salient region detection, center-biased, Gaussian model

CLC Number:

TP391.4

Cai Qiang, Xue Ziyu, Mao Dianhui, Li Haisheng. Salient Region Detection based on Object-Biased Gaussian Refinement and Global Contrast[J]. Journal of System Simulation, 2015, 27(10): 2489-2496.

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