A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering. Issue 4 (20th February 2020)

• Record Type: Journal Article
• Authors: Li, Zhijiang; Zheng, Yingping; Cao, Liqin; Jiao, Lei; Zhong, Yanfei; Zhang, Caiyi
• Is Part Of: Color research & application. Volume 45:Issue 4(2020:Aug.)
• Page Start: 656
• Page End: 670
• Publication Date: 2020-02-20
• Subjects: cluster center selection -- density peaks clustering -- image color clustering -- Student's t kernel function -- superpixel segmentation; Color -- Periodicals; 535
• DOI: 10.1002/col.22491
• Languages: English
• ISSNs: 0361-2317

Abstract

Abstract: Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non‐iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k‐means, fuzzy c‐means, mean‐shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.