Angle-Tree: a new index structure for high-dimensional point data

Daoguo Dong; Xiangyang Xue; Hangzai Luo; Yingqiang Lin

doi:10.1117/12.451114

19 December 2001 Angle-Tree: a new index structure for high-dimensional point data

Daoguo Dong, Xiangyang Xue, Hangzai Luo, Yingqiang Lin

Proceedings Volume 4676, Storage and Retrieval for Media Databases 2002; (2001) https://doi.org/10.1117/12.451114
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Many multi-dimensional index structures, such as R-Tree, R^*-Tree, X-Tree, SS-Tree, VA-File, etc. have been proposed to support similarity search with l₁, l₂ or l_(infinity ) distance as similarity measure. But they can not support such similarity search with cosine as the similarity measure. In this paper, an index structure Angle-Tree is introduced to resolve the problem. It first projects all the high dimensional points onto the unit hyper-spherical surface, i.e. normalize each original vector in the database into a unit one. Then an index structure similar to R-Tree is built for those projected points. The experimental results show that the Angle-Tree can decrease the cost of disk I/O and support fast similarity search.

Citation Download Citation

Daoguo Dong, Xiangyang Xue, Hangzai Luo, and Yingqiang Lin "Angle-Tree: a new index structure for high-dimensional point data", Proc. SPIE 4676, Storage and Retrieval for Media Databases 2002, (19 December 2001); https://doi.org/10.1117/12.451114

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