Efficient High-Dimensional Image Indexing Based on SVD for Quadratic form Distance
二次式距离上基于SVD的高维图像索引方法

Many traditional indexing methods perform poorly in high dimensional vector space.The Vector Approximation File approach overcomes some of the difficulties of dimensionality curse,but it can't support the quadratic form metric.A novel VA-File approach for quadratic form distance is introduced in this paper.By the SVD of similarity matrix,the quadratic form distance can be converted to the Euclidean distance,and the approximation vector can be obtained. The low-dimensional filter algorithm is also applied during the nearest neighbor search.The vectors are first filtered with the low-dimensional approximate distance measure,and then the candidate results are re-computed with high-dimensional distance measure.The experimental results show that it can save the computational time significantly because only a small set of vectors is computed on the high-dimensional distance measure.