TOWARDS ROBUST AND EFFICIENT SEGMENTATION: AN APPROACH BASED ON INTER-REGION CONTOUR AND INTRA-REGION CONTENT ANALYSIS
Zhiding Yu, Oscar Au, Ketan Tang, Lingfeng Xu, Wenxiu Sun, Yuanfang GuoAbstract
We address the problem of boundary estimation by formulating it as inter-region contour and intra-region information analysis in the framework of graph-based segmentation. Given an image without any prior information about object model and class, we seek to approximate one's instant perception of visual similarity. The method can serve as a preprocessing step for many higher level operations that require regional support, such as scene understanding and object recognition. We show in this paper that the defined region comparison predicate makes a better boundary estimator than efficient graph-based image segmentation (EGS) - a well known and widely used segmentation method. We further illustrate, by making a small relaxation, further improvement of segmentation performance can be achieved. Experimental results have demonstrated the effectiveness of our proposed method.
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