Image segmentation by clustering of spatial patterns

Authors:
Yong Xia;(David) Dagan Feng;Tianjiao Wang;Rongchun Zhao;Yanning Zhang
Affiliations:
School of Information Technologies, J12, The University of Sydney, Sydney, NSW 2006, Australia and School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;School of Information Technologies, J12, The University of Sydney, Sydney, NSW 2006, Australia and Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, Ho ...;School of Automation, Northwestern Polytechnical University, Xi'an 710072, China;School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

This letter describes an approach to perceptual segmentation of images through the means of clustering of spatial patterns. An image is modeled as a set of spatial patterns defined on a rectangular lattice. The distance between a spatial pattern and each cluster is defined as a combination of the Euclidean distance in the feature space and the spatial dissimilarity which reflects how much of the pattern's neighbourhood is occupied by other clusters. Our approach has been compared with the Fuzzy C-Mean (FCM) algorithm, a spatial fuzzy clustering algorithm and a Markov Random Field (MRF) based algorithm by segmenting synthetic images, texture mosaics and natural images. The results of those comparative experiments demonstrate that the proposed approach can segment images more effectively and provide more robust segmentation results.