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
Vector quantization and signal compression
Vector quantization and signal compression
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
An efficient indexing method for nearest neighbor searches inhigh-dirnensional image databases
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimedia Tools and Applications
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Semantics supervised cluster-based index for video databases
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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Practical content-based image retrieval systems require efficient indexing schemes for fast searches. Researchers have proposed many methods using space and data partitioning for exact similarity searches. However, traditional indexing methods perform poorly and will degrade to simple sequential scans at high dimensionality - that is so-called "curse of dimensionality". Recently, several filtering approaches based on vector approximation (VA) were proposed and showed promising performance. In fact, existing VA-based methods assume independent distribution of dataset and utilize scalar quantizer to partition each dimension of data space. In real databases, however, images are from different categories and often clustered. In this paper, a novel indexing method using vector quantization is proposed. This approach introduces a vector quantizer to partition data space. It assumes a Gaussian mixture distribution and estimates this distribution through Expectation-Maximization (EM) method. Experiments on a large database of 275,465 images demonstrated a remarkable improvement of retrieval efficiency.