Based on KHA for Extraction of Shift Invariant Multiwavelet Features of Texture Images

In this paper, we propose an based on KHA for extraction of shift invariant multiwavelet features of texture images. The feature extraction process involves a normalization followed by a shift invariant multiwavelet packet transform. The normalization converts a given image into a size invariant image which is then passed to the shift invariant multiwavelet packet transform to generate subbands of shift invariant wavelet coefficients. Then we convert the multiwavelet coefficients matrix to a smaller dimension correlation matrix, and we obtain the KHA coefficients matrix with the rows are eigenvectors of the correlation matrix ordered by decreasing eigenvalue by KHA. An energy signature is computed for each subband of these KHA coefficients. In order to reduce feature dimensionality, only the most dominant wavelet energy signatures are selected as feature vector for classification.