Title
Wavelet transform and texture recognition based on spiking neural network for visual images.
Abstract
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.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.03.086
Neurocomputing
Keywords
Field
DocType
Spiking neural networks,Human visual system,Fast wavelet transform,Image reconstruction,Texture classification
Human visual system model,Computer science,Fast wavelet transform,Artificial intelligence,Spiking neural network,Wavelet,Wavelet transform,Iterative reconstruction,Computer vision,Pattern recognition,Feature extraction,Machine learning,Image compression
Journal
Volume
ISSN
Citations 
151
0925-2312
4
PageRank 
References 
Authors
0.40
17
5
Name
Order
Citations
PageRank
Zhenmin Zhang141.08
Qingxiang Wu21019.98
Zhiqiang Zhuo383.22
Xiao-Wei Wang459659.78
Liuping Huang561.45