Fast k-nearest neighbor search algorithm based on pyramid structure of wavelet transform and its application to texture classification
Traditional fast k-nearest neighbor search algorithms based on pyramid structures need either many extra memories or long search time. This paper proposes a fast k-nearest neighbor search algorithm based on the wavelet transform, which exploits the important information hiding in the transform coefficients to reduce the computational complexity. The study indicates that the Haar wavelet transform brings two kinds of important pyramids. Two elimination criteria derived from the transform coefficients are used to reject those impossible candidates. Experimental results on texture classification verify the effectiveness of the proposed algorithm.
k-nearest neighbor search wavelet transform texture classification vector quantization encoding pattern recognition