Combination of Wavelet snd SIFT Features for Image Classification Using Trained Gaussion Mixture Model

This paper presents an effective combination of Wavelet-based features and SIFT features. For the combined feature patches extracted from images we then adopt the PCA transformation to reduce the dimensionality of their feature vectors. And the reduced vectors are used to train Gaussian Mixture Models (GMMs) in which the mixture weights and Gaussian parameters are updated iteratively. We performed the method on Caltech datasets and compared the results with several other methods. It shown that the combination of salient feature vectors and GMM gives a much better improvement in image classification.