| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 102 === Image segmentation plays an important role in computer vision.The purpose of image segmentation is to partition an image into meaningful regions with homogeneous properties automatically.Lots of literature reviews show that GrabCut is a better method in recent years.GrabCut is a foreground extraction method which uses graph algorithm to compute its energy function.GrabCut divides an image into foreground and background with high accuracy.It is particularly noteworthy that GrabCut defined the energy function in terms of region and boundary properties which is in accord with human visual perception.However, GrabCut is a semi-automatic segmentation method.It is not convenient for users because they must choose some background seed points before using GrabCut to segment images.In this thesis, we propose three automatic segmentation methods with good accuracy rate by adjusting GrabCut's architectures and parameters.These three methods are called ``K-Improvement'', ``K-Ascending'', and ``Epoch-Method''.The experiments are conducted on Microsoft Research Cambridge's database.We use sum of absolute difference, Jaccard similarity coefficient, and F-measure to evaluate the result.The experiment results show that the proposed methods have higher performance than CrabCut.
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