MyFinder: near-duplicate detection for large image collections

Authors:
Xin Yang;Qiang Zhu;Kwang-Ting Cheng
Affiliations:
ECE department, University of California-Santa Barbara, Santa Barbara, CA, USA;ECE department, University of California-Santa Barbara, Santa Barbara, CA, USA;ECE department, University of California-Santa Barbara, Santa Barbara, CA, USA
Venue:
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Year:
2009

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Abstract

The explosive growth of multimedia data poses serious challenges to data storage, management and search. Efficient near-duplicate detection is one of the required technologies for various applications. In this paper, we introduce MyFinder, an image near-duplicate detection system for large image collections. MyFinder consists of three major components: 1) a local-feature-based image representation utilizing the proposed LDP (Local-Difference-Pattern) feature, 2) the Locality-Sensitive-Hashing (LSH) as the core indexing structure to assure the most frequent data access occurred in the main memory, and 3) multi-step verification for queries to best exclude false positives and to increase the precision.