Nearest Neighbor Search with the Revised TLAESA

It is significant to develop better search methods to handle the rapidly increasing volume of multimedia data. For NN (Nearest Neighbor) search in metric spaces, the TLAESA (Tree Linear Approximating and Eliminating Search Algorithm) is a state of art fast search method. In this paper a method is proposed to improve the TLAESA by revising the tree structure with an optimal number of selected global pivots in the higher levels as representatives and employing the best-first search strategy. Based on an improved version of the TLAESA that succeeds in using the best-first search strategy to greatly reduce the distance calculations, this method improves the drawback that calculating less at the price of the lower pruning rate of branches. The lower pruning rate further can lead to lower search efficiency, because the priority queue used in the adopted best-first search strategy stores the information of the visited but unpruned nodes, and need be frequently accessed and sorted. In order to enhance the pruning rate of branches, the improved method tries to make more selected global pivots locate in the higher levels of the search tree as representatives. As more real distances instead of lower bound estimations of the node-representatives are used for approximating the closet node and for “branch and bound”, not only which nodes are close to the query object can be evaluated more effectively, but also the pruning rate of branches can be enhanced. Experiments show that for k-NN queries in Euclidean space, in a proper pivot selection strategy the proposed method can reach the same fewest distance calculations as the LAESA (Linear Approximating and Eliminating Search Algorithm) which saves more calculations than the TLAESA, and can achieve a higher search efficiency than the TLAESA.