中文摘要：

Problems existin similarity measurement and index tree construction which affect the performance of nearest neighbor search of high-dimensional data. The equidistance problem is solved using NPsim function to calculate similarity. And a sequential NPsim matrix is built to improve indexing performance. To sum up the above innovations,a nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set. Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others. In addition,the slow construction speed of sequential NPsim matrix can be increased by using parallel computing.

英文摘要：

Problems existin similarity measurement and index tree construction which affect the perform- ance of nearest neighbor search of high-dimensional data. The equidistance problem is solved using NPsim function to calculate similarity. And a sequential NPsim matrix is built to improve indexing performance. To sum up the above innovations, a nearest neighbor search algorithm of high-dimen- sional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set. Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others. In addition, the slow construction speed of sequential NPsim matrix can be increased by using parallel computing.