Wavelet-based rotational invariant roughness features for texture classification and segmentation

    Authors

    D. Charalampidis;T. Kasparis

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Image Process.

    Keywords

    fractals; k-means; segmentation; texture; wavelets; SURFACES; FILTERS; IMAGES; MODELS; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    In this paper, we introduce a rotational invariant feature set for texture segmentation and classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single-scale features are combined with multiple-scale features for a more complete textural representation. Wavelets are employed for the computation of single- and multiple-scale roughness features because of their ability to extract information at different resolutions. Features are extracted in multiple directions using directional wavelets, and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information. An iterative K-means scheme is used for segmentation, and a simplified form of a Bayesian classifier is used for classification. The use of the roughness feature set results in high-quality segmentation performance. Furthermore, it is shown that the roughness feature set exhibits a higher classification rate than other feature vectors presented in this work. The feature set retains the important properties of FD-based features, namely insensitivity to absolute illumination and contrast.

    Journal Title

    Ieee Transactions on Image Processing

    Volume

    11

    Issue/Number

    8

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    825

    Last Page

    837

    WOS Identifier

    WOS:000177465800001

    ISSN

    1057-7149

    Share

    COinS