Title :
Combination of Wavelet snd SIFT Features for Image Classification Using Trained Gaussion Mixture Model
Author :
Wang, Kejun ; Ren, Zhen ; Xiong, Xinyan
Author_Institution :
Pattern Recognition Lab., Harbin Eng. Univ., Harbin
Abstract :
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.
Keywords :
Gaussian processes; feature extraction; image classification; principal component analysis; wavelet transforms; Caltech datasets; Gaussian mixture model; PCA transformation; SIFT features; image classification; salient feature vectors; scale invariant feature transform; wavelet-based features; Automation; Computer vision; Detectors; Educational institutions; Feature extraction; Gaussian processes; Image classification; Laboratories; Pattern recognition; Principal component analysis; Gaussian mixture models; Image Classification; SIFT Feature; Wavelet-based Feature;
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP '08 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-0-7695-3278-3
DOI :
10.1109/IIH-MSP.2008.76