Texture classification using wavelet decomposition

In this paper, a technique of texture image classification using a wavelet decomposition with selective wavelet packet node decomposition will be discussed. Using strength as a metric, selective wavelet decomposition is controlled. The metric is used to allow further decomposition or to terminate recursive decompositions. Decision of continuing further decompositions is based on each subbandpsilas strength relative to the strengths of other subbands of the same wavelet decomposition level. Once the decompositions stop, the structure of the packet is stored in a data structure of dominating channels to be used later. Using the information from the data structure, dominating channels are extracted. These are defined as paths from the root of the packet to the leaf with the highest strengths. The list of dominating channels are used to train a learning vector quantization neural network.