The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Indexing High-Dimensional Data for Content-Based Retrieval in Large Databases
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
| Hi-index | 0.00 |
To support fast k-NN queries over large image database, in this paper, we propose a novel dual distance transformation method called DDT. In DDT, all images are first grouped into clusters by the k-Means clustering algorithm. Then the start- and centroid-distances of each image are combined to obtain the uniform index key through its dual distance transformation. Finally the keys are indexed by a B+-tree. Thus, given a query image, its k-nearest neighbor query in high-dimensional space is transformed into a search in a single dimensional space with the aid of the DDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results demonstrate that this method outperforms the state-of-the-art high dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree.