Hybrid neural network system for texture analysis

Texture classification and segmentation in digital images is commonly achieved using spatial grey level dependence matrices (SGLDMs), often referred to as co-occurrence matrices. This involves the computation of many matrices over a range of different spatial separations and orientations. The approach proposed in this paper uses a hybrid neural network system, consisting of a self-organising map followed by a backpropagation network, to restrict the number of SGLDMs that need to be computed. The system is trained in two phases on images with known texture content. The trained system is able to provide information, in the form of pixel spacing and orientation, on the texture content of unseen images. This information may be used to select appropriate SGLDMs for further texture classification. Experimental results are presented which demonstrate the effective performance of the system