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Randomized Locality Sensitive Vocabularies for Bag-of-Features Model

  • Yadong Mu
  • Ju Sun
  • Tony X. Han
  • Loong-Fah Cheong
  • Shuicheng Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

Visual vocabulary construction is an integral part of the popular Bag-of-Features (BOF) model. When visual data scale up (in terms of the dimensionality of features or/and the number of samples), most existing algorithms (e.g. k-means) become unfavorable due to the prohibitive time and space requirements. In this paper we propose the random locality sensitive vocabulary (RLSV) scheme towards efficient visual vocabulary construction in such scenarios. Integrating ideas from the Locality Sensitive Hashing (LSH) and the Random Forest (RF), RLSV generates and aggregates multiple visual vocabularies based on random projections, without taking clustering or training efforts. This simple scheme demonstrates superior time and space efficiency over prior methods, in both theory and practice, while often achieving comparable or even better performances. Besides, extensions to supervised and kernelized vocabulary constructions are also discussed and experimented with.

Keywords

Random Forest Hash Function Visual Word Reproduce Kernel Hilbert Space Mean Average Precision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yadong Mu
    • 1
  • Ju Sun
    • 1
    • 2
  • Tony X. Han
    • 3
  • Loong-Fah Cheong
    • 1
    • 2
  • Shuicheng Yan
    • 1
  1. 1.Electrical and Computer EngineeringNational University of SingaporeSingapore
  2. 2.Interactive & Digital Media InstituteNational University of SingaporeSingapore
  3. 3.Electrical and Computer EngineeringUniversity of MissouriColumbiaUSA

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