PCA and LDA Based Fuzzy-Neural-Networks for Image Classification

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The Fuzzy Neural Network (FNN) is applied for Image Classification in this thesis.The image data is processed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA maximizes the variance of the samples in the projection subspace. Consequently, the image data can possesses most of the original characteristic after compression by PCA mapping. LDA uses image class information to find a subspace for better discrimination of different face classes. To reduce the dimension calculation of PCA, the Histogram method is proposed to count the RGB or gray level value of each pixel. The FNN is a classifier in this thesis. The image data after PCA and LDA mapping is the input of the FNN. In the Fuzzification part, we use the Gaussian mixture model (GMM). Parameter learning adopts the gradient method to adjust the shapes of Gaussian functions. The testing part of the FNN is implemented by firmware coded in assembly. The used CPU is designed by our lab. To enhance the efficiency of the program, we develop some instruction for math calculation. In the exponential part, the lookup table is adopted.In contrast to Taylor expansion, the calculation is reduced obviously by the lookup table.