Abstract: The thresholding is an important form of image segmentation and is used in many applications that involve image processing and object recognition. hhus, it is crucial to how to acquire a threshold of image segmentation. A novelmultilevel thresholding algorithm was presented in order to improve image segmentation performance at lower computational cost. hhe proposed algorithm determines the thresholdings by spectral clustering algorithm called Dcut that uses a new similarity function. hhe weight matrices used in evaluating the graph cuts arc based on the gray levels of an image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, proposed algorithm occupies much smaller storage space and requires much lower com- putational costs and implementation complexity than other graph-based image segmentation algorithms. A large number of examples were presented to show the superior performance by using the proposed multilevel thrcsholding algorithm compared to existing thresholding algorithms.

Key words: Image thresholding segmentation, Multilevel thresholding, Spectral clustering, Dcut