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Automatic generation of traditional style painting by using density-based color clustering

Huang, Guangyan, Ding, Zhiming and He, Jing 2007, Automatic generation of traditional style painting by using density-based color clustering, in ICDMW 2007: Proceedings of the 7th IEEE International Conference on Data Mining, IEEE, Piscataway, N.J., pp. 41-44, doi: 10.1109/ICDMW.2007.17.

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Title Automatic generation of traditional style painting by using density-based color clustering
Author(s) Huang, Guangyan
Ding, Zhiming
He, Jing
Conference name IEEE International Conference on Data Mining Workshops (7th : 2007 : Omaha, NE, USA)
Conference location Omaha, Neb.
Conference dates 28-31 Oct. 2007
Title of proceedings ICDMW 2007: Proceedings of the 7th IEEE International Conference on Data Mining
Publication date 2007
Start page 41
End page 44
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Summary In this paper, a novel approach is proposed to automatically generate both watercolor painting and pencil sketch drawing, or binary image of contour, from realism-style photo by using DBSCAN color clustering based on HSV color space. While the color clusters produced by proposed methods help to create watercolor painting, the noise pixels are useful to generate the pencil sketch drawing. Moreover, noise pixels are reassigned to color clusters by a novel algorithm to refine the contour in the watercolor painting. The main goal of this paper is to inspire non-professional artists' imagination to produce traditional style painting easily by only adjusting a few parameters. Also, another contribution of this paper is to propose an easy method to produce the binary image of contour, which is a vice product when mining image data by DBSCAN clustering. Thus the binary image is useful in resource limited system to reduce data but keep enough information of images. © 2007 IEEE.
ISBN 0769530192
9780769530192
ISSN 1550-4786
Language eng
DOI 10.1109/ICDMW.2007.17
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083685

Document type: Conference Paper
Collection: School of Information Technology
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