Genetic cuts for image segmentation

Abstract. The normalized cut (Ncut) method is a popular method for segmenting images and videos. The Ncutmethod segments an image into two disjoint regions, each segmented by the same method. After the Ncutmethod has been recursively applied to an image, its final segmented image is obtained. The main drawbackof the Ncut method is that a user cannot easily determine the stop criteria because users have no idea about thenumber of regions in an image. This work proposes the genetic cut (Gcut) algorithm to resolve this shortcoming.Users do need not to specify thresholds in the Gcut algorithm, which automatically segments an image into theproper number of regions. Also, the neighbor-merging (NM) algorithm is proposed for preprocessing the imagesand improves the performance of the Gcut algorithm. Thus, the proposed Gcut method combines the NM andGcut algorithms.Furthermore, aheuristic methodisproposedto identifyagoodsegment forthe Gcutmethod.Inall experiments, the proposed Gcut method outperforms traditional Ncut methods.