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An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.
Shift invariance, wavelet packet transform, normalization, image classification.

C. Pun and M. Lee, "Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 1228-1233, 2004.
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