BASIL: effective near-duplicate image detection using gene sequence alignment

  • Authors:

  • Hung-sik Kim;Hau-Wen Chang;Jeongkyu Lee;Dongwon Lee

  • Affiliations:

  • Computer Science and Engineering, Penn State University;Computer Science and Engineering, Penn State University;Computer Science and Engineering, University of Bridgeport;College of Information Sciences and Technology, Penn State University

  • Venue:
  • ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
  • Year:
  • 2010

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Abstract

Finding near-duplicate images is a task often found in Multimedia Information Retrieval (MIR). Toward this effort, we propose a novel idea by bridging two seemingly unrelated fields – MIR and Biology. That is, we propose to use the popular gene sequence alignment algorithm in Biology, i.e., BLAST, in detecting near-duplicate images. Under the new idea, we study how various image features and gene sequence generation methods (using gene alphabets such as A, C, G, and T in DNA sequences) affect the accuracy and performance of detecting near-duplicate images. Our proposal, termed as BLASTed Image Linkage (BASIL), is empirically validated using various real data sets. This work can be viewed as the “first” step toward bridging MIR and Biology fields in the well-studied near-duplicate image detection problem.