Automatic Generation of Traditional Style Painting by Using Density-Based Color Clustering

Adafre, Sisay Fissaha; Rijke, Maarten de

doi:10.1109/icdmw.2007.17

Cited by 2 publications

(1 citation statement)

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“…Density-based algorithms have also been widely adopted in image color clustering. [31][32][33][34][35] Compared with the partition-based algorithms, this kind of algorithm is suitable for data with arbitrary shape and does not require preconfiguring of the number of clusters. DBSCAN and mean shift are typical algorithms used in image color clustering.…”

Section: Related Studymentioning

confidence: 99%

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Zheng

Cao

et al. 2020

Color Research & Application

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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.

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Section: Related Studymentioning

confidence: 99%