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Multimedia Tools and Applications

, Volume 51, Issue 2, pp 441–477 | Cite as

Building descriptive and discriminative visual codebook for large-scale image applications

  • Qi Tian
  • Shiliang Zhang
  • Wengang Zhou
  • Rongrong Ji
  • Bingbing Ni
  • Nicu Sebe
Article

Abstract

Inspired by the success of textual words in large-scale textual information processing, researchers are trying to extract visual words from images which function similar as textual words. Visual words are commonly generated by clustering a large amount of image local features and the cluster centers are taken as visual words. This approach is simple and scalable, but results in noisy visual words. Lots of works are reported trying to improve the descriptive and discriminative ability of visual words. This paper gives a comprehensive survey on visual vocabulary and details several state-of-the-art algorithms. A comprehensive review and summarization of the related works on visual vocabulary is first presented. Then, we introduce our recent algorithms on descriptive and discriminative visual word generation, i.e., latent visual context analysis for descriptive visual word identification [74], descriptive visual words and visual phrases generation [68], contextual visual vocabulary which combines both semantic contexts and spatial contexts [69], and visual vocabulary hierarchy optimization [18]. Additionally, we introduce two interesting post processing strategies to further improve the performance of visual vocabulary, i.e., spatial coding [73] is proposed to efficiently remove the mismatched visual words between images for more reasonable image similarity computation; user preference based visual word weighting [44] is developed to make the image similarity computed based on visual words more consistent with users’ preferences or habits.

Keywords

Visual vocabulary Large-scale image retrieval Image search re-ranking Feature space quantization 

Notes

Acknowledgement

This work is supported in part by NSF IIS 1052851 and by Akiira Media Systems, Inc. The work of Nicu Sebe has been supported by the FP7 IP GLOCAL European project and by the FIRB S-PATTERN project.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Qi Tian
    • 1
  • Shiliang Zhang
    • 2
  • Wengang Zhou
    • 3
  • Rongrong Ji
    • 4
  • Bingbing Ni
    • 5
  • Nicu Sebe
    • 6
  1. 1.Computer Science DepartmentUniversity of Texas at San AntonioSan AntonioUSA
  2. 2.Key Lab of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina
  3. 3.EEIS DepartmentUniversity of Science and Technology of ChinaHeifeiChina
  4. 4.Harbin Institute of TechnologyHarbinChina
  5. 5.National University of SingaporeSingaporeSingapore
  6. 6.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly

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