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

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 -means scheme is used for segmentation, and a simplified form of a Bayesian classifier is used for classific...

• Type: Journal
• Year: 2002
• Where: TIP
• Authors: Dimitrios Charalampidis, Takis Kasparis
• Keywords: Feature Vectors, Rotational Invariant Feature, Roughness, TIP 2002