In wavelet packet based image processing and classification, the proper selection of a subset of subbands is crucial for efficient representation of the image with a small number of subbands. Various algorithms have been proposed to address the subband selection problem. However, these algorithms evaluate the representation power of each subband separately based on a pre-defined cost function and subsequently choose a set of subbands based on these representation powers. This process implicitly assumes the independence between different subbands, which seldom holds and thus, degrades the classification performance. Past experience shows that the subbands with high energy play an important role in classifying texture. To incorporate both the dependence between subbands and the individual power of each subband into the subband selection process, we propose a subband grouping and selection (SGS) algorithm for selecting subbands for texture classification. Our experiments show that the proposed subband selection algorithm effectively outperforms the traditional algorithms by achieving higher classification accuracy with a fewer number of subbands.
['Ke Huang', 'Selin Aviyente']
Statistical partitioning of wavelet subbands for texture classification