Linearized circular energy curve color image segmentation based on Tsallis entropy

Abstract

Image processing is a continuously evolving field, and due to the fact that color images contain more information than grayscale images and are the most encountered type of images in daily life, the analysis and processing of color images are particularly important. The use of the H component of the HSI color space model to construct a circular histogram for selecting the optimal threshold is one method in color image segmentation. However, traditional circular histograms only reflect the pixel distribution of the entire image by statistically counting the frequency of pixel occurrences in the entire image, without considering the spatial context information of the image for optimal threshold selection. To address this drawback, we propose the method of circular energy curves (CEC). This method involves calculating energy curves on the H component to construct CEC. The optimal threshold is then selected based on these curves. To reduce the complexity of threshold selection for CEC and enhance their generality, we combine cumulative distribution functions with information energy, introducing the method of cumulative distribution information energy (CDF-IE) to determine the optimal breakpoint. After linearizing the circular energy curves (L-CEC), we use the Tsallis entropy thresholding method to achieve the optimal segmentation threshold selection for the H component CEC. To improve the effectiveness of the algorithm, we present the Tsallis entropy recursive algorithm after L-CEC, which reduces the time complexity. The proposed method in this paper, compared to the circular histogram method, achieves better image segmentation results and exhibits stronger applicability.