NDLI: Statistical Multiscale Image Segmentation via Alpha-Stable Modeling

Content Provider	IEEE Xplore Digital Library
Author	Tao Wan Canagarajah, N. Achim, A.
Copyright Year	2007
Description	Author affiliation: Bristol Univ., Bristol (Tao Wan; Canagarajah, N.; Achim, A.)
Abstract	This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by symmetric alpha-stable (SalphaS) distributions. These features are efficiently combined with the dominant color feature to perform automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using the dual-tree complex wavelet transform (DT-CWT) with the sub-band coefficients modeled as SalphaS random variables. A mul-tiscale segmentation is then applied to the resulting regions, according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring the Kullback-Leibler distance (KLD) between estimated SalphaS models for the neighboring segments. Experiments show that our algorithm achieves superior segmentation results in comparison with existing state-of-the-art image segmentation algorithms.
File Size	894977
File Format	PDF
ISBN	9781424414369
ISSN	15224880
DOI	10.1109/ICIP.2007.4380028
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Wavelet transforms Merging Image processing Partitioning algorithms Image analysis Feature extraction Random variables Signal processing Robustness KLD multiscale image segmentation statistical modeling wavelet transform S¿S distributions
Content Type	Text
Resource Type	Article

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