Lorenz Curve-Based Entropy Thresholding on Circular Histogram

Abstract

Circular histogram thresholding is a new threshold selection method in color image segmentation. However, the method of the existing circular histogram thresholding based on the Otsu Criteria lacks the generality of using the circular histogram. In order to improve the effectiveness and reduce the complexity of thresholding on circular histogram, this paper firstly introduces the Lorenz curve into circular histogram. Then the circular histogram is expanded into the linearized histogram in clockwise or anti-clockwise direction by the optimal index of the Lorenz curve. In the end, the entropy thresholding of the linearized circular histogram is adopted to choose the optimal threshold to obtain the object of color images. Many experimental results show that the proposed method has better effectiveness and adaptability than the existing circular thresholding utilizing Otsu Criteria.