Dyadic frames of directional wavelets as texture descriptors

We propose a wavelet-based texture classification system. Texture descriptors are local energy measures within the feature images obtained by projecting the samples on Dyadic Frames of Directional Wavelets. Rotation invariant features are obtained by taking the Fourier expansion of the subsets of components of the original feature vectors concerning each considered scale separately. Three different classification schemes have been compared: the Euclidean, the weighted Euclidean and the KNN classifiers. Performances have been evaluated on a set of 13 Brodatz textures, from which both a training set and a test set have been extracted. Results are present in the form of confusion matrices. The KNN classifier provides the globally best performance, with an average recognition rate around the 96 percent for the original non-rotated test set, and 88 percent when the rotated versions are considered. Its simplicity and accuracy renders the proposed method highly suited for multimedia applications, as content-based image retrieval.