Saliency Detection based on Global Color Distribution and Active Contour Analysis

• Zhengping Hu,
• Zhenbin Zhang,
• Zhe Sun,
• Shuhuan Zhao,

Vol. 10, No. 12, December 30, 2016
10.3837/tiis.2016.12.020, Download Paper (Free):

• Saliency detection
• active contour model
• SEED
• global color distribution
• affinity matrix
• Bayesian model

Abstract

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

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