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A hierarchical word-merging algorithm with class separability measure

Journal Article


Abstract


  • In image recognition with the bag-of-features model, a small-sized visual codebook is usually preferred to

    obtain a low-dimensional histogram representation and high computational efficiency. Such a visual

    codebook has to be discriminative enough to achieve excellent recognition performance. To create a

    compact and discriminative codebook, in this paper we propose to merge the visual words in a

    large-sized initial codebook by maximally preserving class separability. We first show that this results in a

    difficult optimization problem. To deal with this situation, we devise a suboptimal but very efficient

    hierarchical word-merging algorithm, which optimally merges two words at each level of the hierarchy. By

    exploiting the characteristics of the class separability measure and designing a novel indexing structure,

    the proposed algorithm can hierarchically merge 10,000 visual words down to two words in merely 90

    seconds. Also, to show the properties of the proposed algorithm and reveal its advantages, we conduct

    detailed theoretical analysis to compare it with another hierarchical word-merging algorithm that

    maximally preserves mutual information, obtaining interesting findings. Experimental studies are

    conducted to verify the effectiveness of the proposed algorithm on multiple benchmark data sets. As

    shown, it can efficiently produce more compact and discriminative codebooks than the state-of-the-art

    hierarchical word-merging algorithms, especially when the size of the codebook is significantly reduced.

Authors


  •   Wang, Lei
  •   Zhou, Luping
  •   Shen, Chunhua (external author)
  •   Liu, Lingqiao (external author)
  •   Liu, Huan (external author)

Publication Date


  • 2014

Citation


  • Wang, L., Zhou, L., Shen, C., Liu, L. & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (3), 417-435.

Scopus Eid


  • 2-s2.0-84894519440

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1969

Has Global Citation Frequency


Number Of Pages


  • 18

Start Page


  • 417

End Page


  • 435

Volume


  • 36

Issue


  • 3

Place Of Publication


  • United States

Abstract


  • In image recognition with the bag-of-features model, a small-sized visual codebook is usually preferred to

    obtain a low-dimensional histogram representation and high computational efficiency. Such a visual

    codebook has to be discriminative enough to achieve excellent recognition performance. To create a

    compact and discriminative codebook, in this paper we propose to merge the visual words in a

    large-sized initial codebook by maximally preserving class separability. We first show that this results in a

    difficult optimization problem. To deal with this situation, we devise a suboptimal but very efficient

    hierarchical word-merging algorithm, which optimally merges two words at each level of the hierarchy. By

    exploiting the characteristics of the class separability measure and designing a novel indexing structure,

    the proposed algorithm can hierarchically merge 10,000 visual words down to two words in merely 90

    seconds. Also, to show the properties of the proposed algorithm and reveal its advantages, we conduct

    detailed theoretical analysis to compare it with another hierarchical word-merging algorithm that

    maximally preserves mutual information, obtaining interesting findings. Experimental studies are

    conducted to verify the effectiveness of the proposed algorithm on multiple benchmark data sets. As

    shown, it can efficiently produce more compact and discriminative codebooks than the state-of-the-art

    hierarchical word-merging algorithms, especially when the size of the codebook is significantly reduced.

Authors


  •   Wang, Lei
  •   Zhou, Luping
  •   Shen, Chunhua (external author)
  •   Liu, Lingqiao (external author)
  •   Liu, Huan (external author)

Publication Date


  • 2014

Citation


  • Wang, L., Zhou, L., Shen, C., Liu, L. & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (3), 417-435.

Scopus Eid


  • 2-s2.0-84894519440

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1969

Has Global Citation Frequency


Number Of Pages


  • 18

Start Page


  • 417

End Page


  • 435

Volume


  • 36

Issue


  • 3

Place Of Publication


  • United States