Color segmentation by fuzzy co-clustering of chrominance color features

Hanmandlu, Madasu, Verma, Om Prakash, Susan, Seba and Madasu, V. K. (2013) Color segmentation by fuzzy co-clustering of chrominance color features. Neurocomputing, 120 235-249. doi:10.1016/j.neucom.2012.09.043

Author Hanmandlu, Madasu
Verma, Om Prakash
Susan, Seba
Madasu, V. K.
Title Color segmentation by fuzzy co-clustering of chrominance color features
Journal name Neurocomputing   Check publisher's open access policy
ISSN 0925-2312
Publication date 2013-11-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.neucom.2012.09.043
Volume 120
Start page 235
End page 249
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Formatted abstract
This paper presents a novel color segmentation technique using fuzzy co-clustering approach in which both the objects and the features are assigned membership functions. An objective function which includes a multi-dimensional distance function as the dissimilarity measure and entropy as the regularization term is formulated in the proposed fuzzy co-clustering for images (FCCI) algorithm. The chrominance color cues a* and b* of CIELAB color space are used as the feature variables for co-clustering. The experiments are conducted on 100 natural images obtained from the Berkeley segmentation database. It is observed from the experimental results that the proposed FCCI yields well formed, valid and high quality clusters, as verified from Liu’s F-measure and Normalized Probabilistic RAND index. The proposed color segmentation method is also compared with other segmentation methods namely Mean-Shift, NCUT, GMM, FCM and is found to outperform all the methods. The bacterial foraging global optimization algorithm gives image specific values to the parameters involved in the algorithm.
Keyword Fuzzy co-clustering
Object membership
Feature membership
Validity measure
Bacterial foraging
Color segmentation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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