Clustering Color Image Segmentation Based on Maximum Entropy

DOI

10.2991/iccasm.2012.375How to use a DOI?

Keywords

Color image, Segmentation, Maximum entropy theory, K-means clustering

Abstract

Maximum entropy is meaningful for representing pixels spatial distribution in the image. This paper proposed a new clustering segmentation approach for color image according to the maximum entropy. Firstly, quantize the HSV color space to equal intervals. The probability distribution of pixels in the quantized space can be seen as a random process. Select a slide interval on the histogram to estimate the classes based on the maximum entropy in the color space. Then observed class number and the initial cluster center. Segmented pixels in to regions by clustering and used spatial filtering to eliminate meaningless regional and holes. The experiment results has shown that this algorithm achieved a good segmentation.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haifeng Sima
AU  - Lanlan Liu
PY  - 2012/08
DA  - 2012/08
TI  - Clustering Color Image Segmentation Based on Maximum Entropy
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1466
EP  - 1468
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.375
DO  - 10.2991/iccasm.2012.375
ID  - Sima2012/08
ER  -