Abstract
An efficient multiresolution texture classification method is proposed in this paper, based on 2-D linear prediction, multiresolution decomposition and artificial neural networks. A multiresolution spectral analysis of textured images is first developed, which permits 2-D AR texture modelling to be performed in multiple resolutions. Recursive estimation algorithms combined witth the Itakura distance measure provide sets of AR model parameters representing different textures at various resolutions. Appropriate neural network banks are constructed and trained being then able to effectively perform classification of textures irrespective of their resolution level. Results are presented using real textured images which illustrate the good performance of the proposed approach.
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