By Topic

Mixture clustering using multidimensional histograms for skin detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zhouyu Fu ; Inst. of Autom., Chinese Acad. of Sci., Beijing, China ; Jinfeng Yang ; Weiming Hu ; Tieniu Tan

Mixture models are frequently used to fit skin color distributions in various color spaces. However, the high computational cost of the conventional EM algorithm makes it intractable for large data sets. We propose a novel algorithm for estimating the parameters of mixture models. Multidimensional histograms are incorporated into the EM framework to group neighboring datapoints and reduce the size of the data set. We adopt this method to build Gaussian mixture models of skin color and compare the performance of models with different number of components. Further experiments on synthetic data show the efficiency of our method as a general approach to data clustering.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:4 )

Date of Conference:

23-26 Aug. 2004