Multi-Value Image Segmentation Based On Fcm Algorithm And Graph Cut Theory

Image segmentation is an important issue in computer vision. The methods based on Fuzzy C-Means (FCM) algorithms have gained success. However, these approaches deal with each pixel as a separate object, which will ignore the spatial information among these pixels. This paper proposes an approach which combines the Fuzzy C-Means algorithm and Graph Cut Theory both for gray and color image segmentation. We adopt the Turbopixel algorithm to split the color image into varied small regions called superpixels for presegmentation and extract color histogram features from the superpixels. Based on color histogram feature, we use FCM to make the original clusters. Then we build a graph model, and use maximum flow algorithm to get the minimum cut, namely the initial segmentation result of the image. Finally, we use a recursive process to achieve the result of image segmentation. The key point of our approach is building a great graphical model and utilizing the existing binary segmentation model to solve the multi-value segmentation. Experimental results show that our approach can obtain good segmentation results comparing with FCM only under different parameters setup and binary segmentation.