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
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Computing Surveys (CSUR)
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
A Pseudo-Metric for Weighted Point Sets
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-preserving Embedding of Proteins
Cluster-preserving Embedding of Proteins
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Reference-based indexing of sequence databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Selecting vantage objects for similarity indexing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Reduction of distance computations in selection of pivot elements for balanced GHT structure
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Hi-index | 0.00 |
Indexing has become a key element in the pipeline of a multimedia retrieval system, due to continuous increases in database size, data complexity, and complexity of similarity measures. The primary goal of any indexing algorithm is to overcome high computational costs involved with comparing the query to every object in the database. This is achieved by efficient pruning in order to select only a small set of candidate matches. Vantage indexing is an indexing technique that belongs to the category of embedding or mapping approaches, because it maps a dissimilarity space onto a vector space such that traditional access methods can be used for querying. Each object is represented by a vector of dissimilarities to a small set of m reference objects, called vantage objects. Querying takes place within this vector space. The retrieval performance of a system based on this technique can be improved significantly through a proper choice of vantage objects. We propose a new technique for selecting vantage objects that addresses the retrieval performance directly, and present extensive experimental results based on three data sets of different size and modality, including a comparison with other selection strategies. The results clearly demonstrate both the efficacy and scalability of the proposed approach.