Abstract
Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.
Highlights
An important low-level image feature used in human perception as well as in recognition is texture
Its main features are (1) a new rotation-invariant and scale-invariant image descriptor, as well as (2) a recent multiclass recognition method based on optimum-path forest
The proposed image descriptor exploits the discriminatory properties of the steerable pyramid decomposition for texture characterization
Summary
An important low-level image feature used in human perception as well as in recognition is texture. The basic property presented in every texture consists in a small elementary pattern repeated periodically or quasiperiodically in a given region (pixel neighborhood) [9, 10]. The repetition of those image patterns generates some visual cues, which can be identified, for example, as being directional or nondirectional, smooth or rough, coarse or fine, uniform or nonuniform [11, 12]. Note that each texture can be associated with one or more visual cues
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