Wavelet transform and texture recognition based on spiking neural network for visual images

The functionalities of spiking neurons can be applied to deal with biological stimuli and explain complicated intelligent behaviors of the brain. The wavelet transforms are widely used in image feature extraction and image compression. Based on the principles from the visual system and wavelet theory, spiking neural networks with the ON/OFF neuron pathways inspired from the human visual system are proposed to perform the fast wavelet transform and the reconstruction for visual images. By this way we try to simulate how the human brain uses the volition-controlled method to extract useful image information. Furthermore, we decompose each texture sample with the established networks and calculate the normalized energy of the obtained sub-images at different scales. These energy values are used as features for texture classification. The simulation results show that the spiking neural network can extract the main information of images so that the images can be accurately classified using the information.