Title

Transfer Non-Metric Measures Into Metric For Similarity Search

Keywords

Multimedia retrieval; Non-metric similarity; Relevance feedback

Abstract

Similarity search is widely used in multimedia retrieval systems to find the most similar ones for a given object. Some similarity measures, however, are not metric, leading to existing metric index structures cannot be directly used. To address this issue, we propose a simulated-annealing-based technique to derive optimized mapping functions that transfer non-metric measures into metric, and still preserve the original similarity orderings. Then existing metric index structures can be used to speed up similarity search by exploiting the triangular inequality property. The experimental study confirms the efficacy of our approach. Copyright 2009 ACM.

Publication Date

12-28-2009

Publication Title

MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Number of Pages

693-696

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1631272.1631390

Socpus ID

72449203660 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/72449203660

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