Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15210
Title: Multifractal signature estimation for textured image segmentation
Authors: Xia, Y
Feng, D
Zhao, R
Zhang, Y
Keywords: Fractal dimension
Image segmentation
Image texture analysis
Mathematical morphology
Multifractal dimensions
Multifractal signature
Issue Date: 2010
Publisher: North-Holland
Source: Pattern recognition letters, 2010, v. 31, no. 2, p. 163-169 How to cite?
Journal: Pattern recognition letters 
Abstract: Fractal theory provides a powerful mathematical tool for texture segmentation. However, in spite of their increasing popularity, traditional fractal features are intrinsically of less accuracy due to the difference between the idea fractal model and the fractal reality of digital images. In this paper, we incorporated the multifractal analysis method into the idea of fractal signature, and thus proposed a novel type of texture descriptor called multifractal signature, which characterizes the variation of multifractal dimensions over spatial scales. In our approach, the local multifractal dimension of each scale was calculated by using the measurement acquired at two successive scales so that the time-consuming and less accurate least square fit was avoided. Based on three popular multifractal measurements, the differential box-counting (DBC) based multifractal signature, relative DBC based multifractal signature, and morphological multifractal signature were presented in this paper. The performance of the proposed texture descriptors was evaluated for segmentation of texture mosaics by comparing to the corresponding multifractal dimensions. The experimental results demonstrated that multifractal signatures can differentiate textured images more effectively and provide more robust segmentations.
URI: http://hdl.handle.net/10397/15210
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2009.09.028
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