Rahkar Farshi Taymaz, Demirci Recep, Feizi-Derakhshi Mohammad-Reza
Computer Engineering Department, Technology Faculty, Gazi University, Ankara 06500, Turkey.
Department of Computer Engineering, University of Tabriz, Tabriz 51666, Iran.
Entropy (Basel). 2018 Apr 18;20(4):296. doi: 10.3390/e20040296.
In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is a typical issue. Moreover, the conventional thresholding algorithms could not be used in color image segmentation. In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering. Thus, initially, threshold selection techniques such as the Otsu and Kapur methods were employed for each color channel separately. The objective functions of both approaches have been integrated with the forest optimization algorithm (FOA) and particle swarm optimization (PSO) algorithm. In the next stage, thresholds determined by optimization algorithms were used to divide color space into small cubes or prisms. Each sub-cube or prism created in the color space was evaluated as a cluster. As the volume of prisms affects the homogeneity of the clusters created, multiple thresholds were employed to reduce the sizes of the sub-cubes. The performance of the proposed method was tested with different images. It was observed that the results obtained were more efficient than conventional methods.
在图像聚类中，期望分配到同一类别的像素必须相同或相似。换句话说，一个聚类的同质性必须很高。在灰度图像分割中，通过增加阈值的数量来实现指定的目标。然而，确定多个阈值是一个典型问题。此外，传统的阈值算法不能用于彩色图像分割。在本研究中，提出了一种新的具有多级阈值的彩色图像聚类算法，并展示了如何将多级阈值技术用于彩色图像聚类。因此，最初，分别对每个颜色通道采用诸如大津法和卡普尔法等阈值选择技术。这两种方法的目标函数已与森林优化算法（FOA）和粒子群优化（PSO）算法相结合。在下一阶段，由优化算法确定的阈值用于将颜色空间划分为小立方体或棱柱体。在颜色空间中创建的每个子立方体或棱柱体都被评估为一个聚类。由于棱柱体的体积会影响所创建聚类的同质性，因此采用多个阈值来减小子立方体的尺寸。用不同的图像测试了所提出方法的性能。观察到所获得的结果比传统方法更有效。
