基于软计算方法的DCT纹理分类
DCT Based Texture Classification Using Soft Computing Approach
摘要：
纹理模式分类是模式识别中的一个重要问题。本文提出了一种基于纹理图像离散余弦变换（DCT）系数的分类方法。由于DCT变换是对灰度图像进行的，因此每幅图像的配色方案都被变换成灰度级。对于使用DCT的图像分类，我们使用了两种流行的软计算技术，即神经计算和神经模糊计算技术。我们使用了一个前馈神经网络训练使用反向传播学习和进化模糊神经网络的纹理分类。软计算模型使用80%的纹理数据进行训练，其余用于测试和验证目的。针对纹理分类问题，对软计算模型进行了性能比较。我们还分析了神经网络长时间训练的效果。实验结果表明，所提出的神经模糊模型的性能优于神经网络。
英文摘要：
Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images using DCT we used two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing. We used a feedforward neural network trained using the backpropagation learning and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. We also analyzed the effects of prolonged training of neural networks. It is observed that the proposed neuro-fuzzy model performed better than neural network.
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