Title :
An improved algorithm for auto-generating digital oil painting based on an accelerated K-means
Author :
Su, Qinghua ; Huang, Zhangcan ; Wang, Xiaohong ; Hu, Zongbo
Abstract :
The color quantization is the first and key step to auto-generate digital oil painting. In this paper, we apply an accelerated K-means based on the triangle equality to implement the image color quantization and then present an algorithm for auto-generating digital oil painting based on the accelerated K-means, called AkM-AgDOP. Comparing the traditional K-means, the accelerated K-means improves more greatly the convergence speed of the algorithm for auto-generating digital oil painting. And the numerical experiments also show that an original image could be transformed into a flowing line image and a better oil effective image with less distortion in color by AkM-AgDOP.
Keywords :
art; computational geometry; image colour analysis; quantisation (signal); AkM-AgDOP; accelerated k-means; color distortion; color quantization; digital oil painting auto-generation; flowing line image; image color quantization; triangle equality; Acceleration; Algorithm design and analysis; Clustering algorithms; Convergence; Image color analysis; Painting; Quantization; accelerated k-means; color quantization; digital oil painting; triangle inequality;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
DOI :
10.1109/FSKD.2012.6234086