Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
C2VA: Trim High Dimensional Indexes
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
An Adaptive Index Structure for High-Dimensional Similarity Search
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
An efficient indexing method for nearest neighbor searches inhigh-dirnensional image databases
IEEE Transactions on Multimedia
The GC-tree: a high-dimensional index structure for similarity search in image databases
IEEE Transactions on Multimedia
Efficient nearest neighbor query based on extended B+-tree in high-dimensional space
Pattern Recognition Letters
| Hi-index | 0.10 |
The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method for image retrieval in large database. Some extensions of VA-file have been proposed towards better query performance. However, all of these methods applying sequential scan need read the whole vector approximation file. In this paper, we present a new indexing structure based on vector approximation method, in which only a small part of approximation file need be accessed. First, principal component analysis is used to map multidimensional points to a 1D line. Then a B^+-tree is built to index the approximate vector according to principal component. When performing k-nearest neighbor search, the partial distortion searching algorithm is used to reject the improper approximate vectors. Only a small set of approximate vectors need to be sequentially scanned during the search, which can reduce the CPU cost and I/O cost dramatically. Experiment results on large image databases show that the new approach provides a faster search speed than the other VA-file approaches.