IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Searching in metric spaces with user-defined and approximate distances
ACM Transactions on Database Systems (TODS)
DynDex: a dynamic and non-metric space indexer
Proceedings of the tenth ACM international conference on Multimedia
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
Efficient Similarity Search in Nonmetric Spaces with Local Constant Embedding
IEEE Transactions on Knowledge and Data Engineering
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Most multimedia information retrieval systems use an indexing scheme to speed up similarity search. The index aims to discard large portions of the data collection at query time. Generally, these approaches use the triangular inequality to discard elements or groups of elements, thus requiring that the comparison distance satisfies the metric postulates. However, recent research shows that, for some applications, it is appropriate to use a non-metric distance, which can give more accurate judgments about the similarity of two objects. In such cases, the lack of the triangle inequality makes impossible to use the traditional approaches for indexing. In this paper we introduce the CP-index, a new approximate indexing technique for non-metric spaces that combines clustering and pivots. The index dynamically adapts to the conditions of the non-metric space using pivots when the fraction of triplets that break the triangle inequality is small, but sequentially searching the most promising candidates when the pivots becomes ineffective.