Texture classification with combined rotation and scale invariant wavelet features

In this work, a new rotational and scale invariant feature set for textural image classification, combined invariant feature (CIF) set has been introduced. It is an integration of the crude wavelets like Gaussian, Mexican Hat and orthogonal wavelets like Daubechies to achieve a high quality rotational and scale invariant feature set. Also it is added with features obtained using the newly proposed weighted smoothening Gaussian filter masks to improve the classification results. To reduce the effect of overlapping features, the variations among the feature set are analyzed and the eigenfeatures are extracted to produce good classification result. The rotational invariance is achieved by using these two wavelets with their directional properties and the scale invariance is achieved by a method, which is an extension to fractal dimension (FD) features. The first- and second-order statistical parameter and entropy characterize the quality of the features extracted. Furthermore, a comparison that shows the higher recognition rate achieved with the newly proposed method for the set of 6720 samples collected from 105 different textures of Brodatz, Vistek, Indezine databases and some additional images collected from other resources of indexed and true color images is shown.

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