Learning and generating color textures with recurrent multiple class random neural networks
Erol Gelenbe, Khaled F. Hussain · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
We propose a method for learning and generating image textures based on learning the weights of a recurrent Multiple Class Random Neural Network (MCRNN) from the color texture image. The network we use has a neuron which corresponds to each image pixel, and the local connectivity of the neurons reflects the adjacent structure of neighboring neurons. The same trained recurrent network is then used to generate a synthetic texture that imitates the original one. The proposed texture learning technique is efficient and its computation time is small. Texture generation is also fast. This work is a refinement and extension of our earlier work where we considered learning of grey-level textures and the generation of grey level or color textures. We have tested our method with different synthetic and natural textures. The experimental results show that the MCRNN can efficiently model a large category of color homogeneous microtextures. Statistical feature extracted from the co-occurrence matrix of the original and the MCRNN based texture are used to confirm the quality of fit of our approach.