Texture Image Segmentation Based on Improved Wavelet Neural Network

Authors:
Deng-Chao Feng;Zhao-Xuan Yang;Xiao-Jun Qiao
Affiliations:
Institute of Electronic & Information Engineering, Tianjin University, Tianjin, 300072, P.R. China and National Engineering Research Center for Information Technology in Agriculture, Beijing, 1000 ...;Institute of Electronic & Information Engineering, Tianjin University, Tianjin, 300072, P.R. China;National Engineering Research Center for Information Technology in Agriculture, Beijing, 100089, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

In this paper, a texture image segmentation algorithm based on improved wavelet neural network is proposed.This algorithm can overcome shortcomings of traditional threshold segmentation techonologies. By using texture features of images, a series of fractal texture feature parameters which will be taken as input layer factors of wavelet network are created by this algorithm. Then, the wavelet neural network is trained with self-adaptive pheromone volatilization mechanism and dynamic heuristic search strategy of improved ant colony algorithm. Finally, the trained wavelet neural network is taken as the classifier of image pixel to realize segmentation of texture images. Simulation experiment shows that, improved algorithm could realize self-adaptive segmentation based on different texture features of images and it is robuster. However, further researches on methods of improving convergence speed of this algorithm and objective criteria for assessing whether texture images have been segmented successfully or not are needed.