Texture image recognition based on bispectrum slice and BP neural network ensembles

Zhengjian Ding, Yasheng Yu
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引用次数: 1

Abstract

To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.
基于双谱切片和BP神经网络集成的纹理图像识别
为了获得纹理图像中三个或多个像素之间的空间关系,选择双谱提取图像的纹理特征,它包含图像的幅度信息和相位信息。由于神经网络存在分类器设计、组态、训练不稳定等问题,基于神经网络集成的研究应运而生。与单个神经网络相比,神经网络集成具有更好的容错能力和泛化能力。本文利用双谱提取纹理特征,利用神经网络集成对纹理图像进行识别。实验结果表明,BP神经网络集成能有效提高纹理图像的正确识别率。
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