Segmentation of textured images based on fractals and image filtering

    Authors

    T. Kasparis; D. Charalampidis; M. Georgiopoulos;J. Rolland

    Comments

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    Abbreviated Journal Title

    Pattern Recognit.

    Keywords

    texture segmentation; Gabor filters; fractal features; energy features; K-means; GABOR FILTERS; CLASSIFICATION; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    This paper describes a new approach to the segmentation of textured gray-scale images based on image pre-filtering and fractal features. Traditionally, filter bank decomposition methods consider the energy in each band as the textural feature, a parameter that is highly dependent on image intensity. In this paper, we use fractal-based features which depend more on textural characteristics and not intensity information. To reduce the total number of features used in the segmentation, the significance of each feature is examined using a test similar to the F-test, and less significant features are not used in the clustering process. The commonly used K-means algorithm is extended to an iterative K-means by using a variable window size that preserves boundary details. The number of clusters is estimated using an improved hierarchical approach that ignores information extracted around region boundaries. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

    Journal Title

    Pattern Recognition

    Volume

    34

    Issue/Number

    10

    Publication Date

    1-1-2001

    Document Type

    Article

    Language

    English

    First Page

    1963

    Last Page

    1973

    WOS Identifier

    WOS:000170417200007

    ISSN

    0031-3203

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