The CMVAI-File: An Efficient Approximation-Based High-Dimensional Index Structure
Similarity-Search based index structure is an important topic in many application areas, such as contentbased multimedia information retrieval, data mining, cluster analysis, etc. In order to improve query performance in highdimensional vector spaces, lots of index structures have been proposed. However, many well-known index structures encounter “dimensional curse while the dimension of feature vector increase. In order to solve such problem, researchers analyzed the nearest-neighbor search problem in highdimensional vector spaces deeply and proposed some approximation-based index structures. The VA-File which is the first implement approximation-based index structures has done well in solving “dimensional curse problem. However, approximate vectors in VA-File are just simply placed in flat structure files without other optimization methods. In this paper, we propose the Collecting and Multi-Vector-Approximation Indexed-File (CMVAI-File). The CMVAI-File is still an approximate-based index structure and uses a filterbased approach. Unlike the VA-File, the CMVAI-File uses multi-level approximate, collect the same vectorapproximations and a segmental structure to improve query performance. Experimental results show that CMVAI-File has a promising improvement in performance.
Similarity-Search High-Dimensional Index Structure Approximation-Based
Lihong Ye Yuan Hua
Communication and Computer Network Key Lab of Guangdong South China University of Technology Guangzhou, China
国际会议
南京
英文
710-713
2010-11-01（万方平台首次上网日期，不代表论文的发表时间）