Bitmap indexing method for complex similarity queries with relevance feedback
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
The Active Vertice method: a performant filtering approach to high-dimensional indexing
Data & Knowledge Engineering
Array-index: a plug&search K nearest neighbors method for high-dimensional data
Data & Knowledge Engineering
Filter ranking in high-dimensional space
Data & Knowledge Engineering
A non-linear dimensionality-reduction technique for fast similarity search in large databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A hierarchical bitmap indexing method for content based multimedia retrieval
IMSA'06 Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications
Hierarchical Indexing Structure for Efficient Similarity Search in Video Retrieval
IEEE Transactions on Knowledge and Data Engineering
An efficient indexing structure for content based multimedia retrieval with relevance feedback
Proceedings of the 2007 ACM symposium on Applied computing
Efficient high-dimensional indexing by sorting principal component
Pattern Recognition Letters
SDI: a swift tree structure for multi-dimensional data indexing in peer-to-peer networks
Proceedings of the 2nd international conference on Scalable information systems
The MPEG-7 Multimedia Database System (MPEG-7 MMDB)
Journal of Systems and Software
KpyrRec: a recursive multidimensional indexing structure
International Journal of Parallel, Emergent and Distributed Systems
Fast search in large-scale image database using vector quantization
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
iPoc: a polar coordinate based indexing method for nearest neighbor search in high dimensional space
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Searching the video: an efficient indexing method for video retrieval in peer to peer network
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Indexing structures for content-based retrieval of large image databases: a review
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Kernel principal component analysis for content based image retrieval
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
VA-files vs. r*-trees in distance join queries
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
QuEval: beyond high-dimensional indexing à la carte
Proceedings of the VLDB Endowment
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Nearest neighbor (NN) search is emerging as an important search paradigm in a variety of applications in which objects are represented as vectors of d numeric features. However, despite decades of efforts, except for the filtering approach such as the VA-file, the current solutions to find exact kNNs are far from satisfactory for large d. The filtering approach represents vectors as compact approximations and by first scanning these smaller approximations, only a small fraction of the real vectors are visited. In this paper, we introduce the local polar coordinate file (LPC-file) using the filtering approach for nearest-neighbor searches in high-dimensional image databases. The basic idea is to partition the vector space into rectangular cells and then to approximate vectors by polar coordinates on the partitioned local cells. The LPC information significantly enhances the discriminatory power of the approximation. To demonstrate the effectiveness of the LPC-file, we conducted extensive experiments and compared the performance with the VA-file and the sequential scan by using synthetic and real data sets. The experimental results demonstrate that the LPC-file outperforms both of the VA-file and the sequential scan in total elapsed time and in the number of disk accesses and that the LPC-file is robust in both "good" distributions (such as random) and "bad" distributions (such as skewed and clustered)