27 February 2007 Wavelet-based texture image classification using vector quantization
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Abstract
Classification of image segments on textures can be helpful for target recognition. Sometimes target cueing is performed before target recognition. Textures are sometimes used to cue an image processor of a potential region of interest. In certain imaging sensors, such as those used in synthetic aperture radar, textures may be abundant. The textures may be caused by the object material or speckle noise. Even speckle noise can create the illusion of texture, which must be compensated in image pre-processing. In this paper, we will discuss how to perform texture classification but constrain the number of wavelet packet node decomposition. The new approach performs a twochannel wavelet decomposition. Comparing the strength of each new subband with others at the same level of the wavelet packet determines when to stop further decomposition. This type of decomposition is performed recursively. Once the decompositions stop, the structure of the packet is stored in a data structure. 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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric P. Lam, Eric P. Lam, } "Wavelet-based texture image classification using vector quantization", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970N (27 February 2007); doi: 10.1117/12.704986; https://doi.org/10.1117/12.704986
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