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A Multi-round Global Performance Evaluation Method for Interactive Image Retrieval

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

In interactive image retrieval systems, from the image search results, a user can select an image and click to view its similar or related images until he reaches the targets. Existing evaluation approaches for image retrieval methods only focus on local performance of single-round search results on some selected samples. We propose a novel approach to evaluate their performance in the scenario of interactive image retrieval. It provides a global evaluation considering multi-round user interactions and the whole image collection. We model the interactive image search behaviors as navigation on an information network constructed by the image collection by using images as graph nodes. We leverage the properties of this constructed image information network to propose our evaluation metrics. We use a public image dataset and three image retrieval methods to show the usage of our evaluation approach.

Keywords

Image retrieval Information network Evaluation 

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References

  1. 1.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) 40(2), 1–60 (2008)CrossRefGoogle Scholar
  2. 2.
    Huiskes, M.J., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval (MIR 2008), pp. 39–43. ACM, New York (2008)Google Scholar
  3. 3.
    Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on CSVT. 8(5), 644–655 (1998)Google Scholar
  4. 4.
    Porkaew, K., Mehrotra, S., Ortega, M.: Query reformulation for content based multimedia retrieval in MARS. In: Proceedings of the IEEE International Conference on Multimedia Computing and Systems (ICMCS 1999), vol. 2, pp. 747–751. IEEE Computer Society, Washington, DC (1999)Google Scholar
  5. 5.
    Tang, X.O., Liu, K., Cui, J.Y., Wen, F., Wang, X.G.: IntentSearch: Capturing User Intention for One-Click Internet Image Search. IEEE Trans. on PAMI 34(7), 1342–1353 (2012)CrossRefGoogle Scholar
  6. 6.
    West, R., Leskovec, J.: Human wayfinding in information networks. In: Proceedings of the 21st International Conference on World Wide Web (WWW 2012), pp. 619–628. ACM, New York (2012)Google Scholar
  7. 7.
    Jain, V., Varma, M.: Learning to re-rank: query-dependent image re-ranking using click data. In: Proceedings of the 20th International Conference on World Wide Web (WWW 2011), pp. 277–286. ACM, New York (2011)Google Scholar
  8. 8.
    Kleinberg, J.: The small-world phenomenon: an algorithmic perspective. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing (STOC 2000), pp. 163–170. ACM, New York (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan

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