基于支持向量机的小波分解的图像模糊分类方法

An Image Fuzzy Classification Method Based on Support Vector Machine (SVM) Wavelet Decomposition

摘要: 针对模糊图像的分类问题，提出了一种基于支持向量机的小波分解的图像模糊分类方法。采集样本后，首先，对图像进行灰度处理，其次对图像进行小波分解，获取第二层的水平、垂直、对角三个方向的信息，之后对图像进行傅里叶对数变换，得出三个方向的频谱信息，随即利用合适的阈值对频谱图像进行二值化，然后对频谱中的连通域边框标记处理，计算长宽比，以长宽比作为训练标准进行网络训练。实验结果表明，该方法能够大大减少模糊图像分类的计算量，在对模糊图像进行处理和分类时有很高的准确度。

Abstract: To solve the problem of fuzzy image classification, an image fuzzy classification method based on support vector machine (SVM) wavelet decomposition was proposed. After collecting the samples, firstly, the image is processed in grayscale, and then the image is decomposed in wavelet transform to obtain the horizontal, vertical and diagonal messages of the second layer. After that, Fourier logarithm transform is performed on the image to obtain the spectral information in three directions, and then appropriate threshold is used to carry out the spectral image. Then, the connected domain border markers in the spectrum are processed, the aspect ratio is calculated, and the aspect ratio is taken as the training standard for network training. The experimental results show that this method can greatly reduce the computation of fuzzy image classification and has high accuracy in processing and classifying fuzzy images.