An Efficient Object Extraction with Graph-Based Image Segmentation

Object extraction process is a closely related issue with image segmentation process. To separate an image to several segments formed similar pixels, many methods are proposed in the area of image processing. Graph-based image segmentation is also one of the segmentation methods. Because of their representation convenience and ease of use, graphs are used as important tools in many image processing applications. While an image segmentation process runs, the processes splitting a graph to sub graphs and merging sub graphs are carried out in the meanwhile. To fulfill these processes, the method uses some local features such as differences between vertices in the graph, which represent pixels, or global features of the image and its segments. To extract an object from an image, we first segmented the entire image, because of to save global features, or to obtain more accurate segmentation. Finally, we extract the intended object from the image by merging the segments that are inside the area drawn before by us. We test the method on some images in the Segmentation Evaluating Database from Weizmann Institute of Science and evaluate the segmentation results. Our F-measure score values show that it seems noticeable good segmentation.