Fast visual retrieval using accelerated sequence matching

Mei Chen Yeh*, Kwang Ting Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


We present an approach to represent, match, and index various types of visual data, with the primary goal of enabling effective and computationally efficient searches. In this approach, an image/video is represented by an ordered list of feature descriptors. Similarities between such representations are then measured by the approximate string matching technique. This approach unifies visual appearance and the ordering information in a holistic manner with joint consideration of visual-order consistency between the query and the reference instances, and can be used for automatically identifying local alignments between two pieces of visual data. This capability is essential for tasks such as video copy detection where only small portions of the query and the reference videos are similar. To deal with large volumes of data, we further show that this approach can be significantly accelerated along with a dedicated indexing structure. Extensive experiments on various visual retrieval and classification tasks demonstrate the superior performance of the proposed techniques compared to existing solutions.

Original languageEnglish
Article number5643930
Pages (from-to)320-329
Number of pages10
JournalIEEE Transactions on Multimedia
Issue number2
Publication statusPublished - 2011 Apr


  • Image classification
  • similarity measure
  • string matching
  • video retrieval

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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