文献类型：期刊文献

英文题名：Image clustering algorithm using superpixel segmentation and non-symmetric Gaussian-Cauchy mixture model

作者：Ji, Sifan[1];Zhu, Hongqing[1];Wang, Pengyu[1];Ling, Xiaofeng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：14

期号：16

起止页码：4132

外文期刊名：IET IMAGE PROCESSING

收录：;EI(收录号：20211210113783);WOS:【SCI-EXPANDED(收录号:WOS:000629318400013)】；

基金：The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Nature Science Foundation of China under grant no. 61872143 and the Natural Science Foundation of Shanghai under grant no. 19ZR1413400.

语种：英文

外文关键词：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.