Texture classification and retrieval using the random neural network model

We extend the application of the random neural network to texture classification and retrieval. A neuron in the network corresponds to an image pixel and the neurons are connected according to neighboring relationship between pixels. A texture is represented with the weights of the network and the random neural network is used as an associative memory. In order to assess the performance of the method, texture mosaic images are generated from the popular Brodatz album. We also present a real life application in which a specified texture type is retrieved within a large remote sensing image. Results show that error is comparable to that of previous studies on texture classification which make use of other approaches.