Image clustering algorithm using superpixel segmentation and non-symmetric Gaussian-Cauchy mixture model

作     者：Ji, Sifan Zhu, Hongqing Wang, Pengyu Ling, Xiaofeng 

作者机构：East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第16期

页      面：4132-4143页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Nature Science Foundation of China Natural Science Foundation of Shanghai [19ZR1413400] 

主　　题：image colour analysis Gaussian distribution image segmentation pattern clustering mixture models image classification computer vision fuzzy set theory entropy unsupervised learning image clustering algorithm-based superpixel segmentation nonsymmetric Gaussian-Cauchy mixture model computer vision research unsupervised clustering algorithm superpixel density images novel superpixel segmentation algorithm Kullback-Leibler divergence good boundary adherence intensity homogeneity Gaussian distribution KL divergence fuzzy objective function generated superpixel intensity images clustering data nonsymmetric mixture model natural colour images newly generated data clustering model 

摘      要：In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback-Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm.