Scaled and rotated texture classification using a class of basis functions

Abstract Three classes of basis functions are considered for classifying scaled and rotated textured images. The first is the orthonormal, compactly supported Daubechies and the discrete Haar bases, the second is the biorthogonal basis and the third is the non orthogonal Gabor basis. Textures are scaled and rotated and the basis functions are used to expand them. Features are computed on a combination of inter-resolution coefficients. Experimental results show that the Daubechies orthonormal basis perform well in recognizing transformed textures, followed by the Haar basis. The concept of multiresolution representation and orthogonality are shown to be useful for invariant texture classificaiton.

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