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Similarity ranking technique exploiting the structure of similarity relationships

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Abstract

This paper proposes a similarity ranking technique that exploits the entire network structure of similarity relationships for multimedia, particularly image, databases. The main problem in the similarity ranking on multimedia is the meaning gap between the characteristics automatically computed from the multimedia dataset and the interpretation by human from the multimedia itself. In fact, the similarity semantics usually lies on high level human interpretation and automatically computed low level multimedia properties may not reflect it. This paper assumes that the meaning of the multimedia is affected by the context or similarity relationships in a dataset and therefore, we propose the ranking technique to catch the semantics from a large multimedia dataset. This similarity ranking technique based on the context or similarity relationships yields better experimental results than the conventional similarity ranking techniques.

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References

  1. Ishikawa Y, Subramanya R, Faloutsos C (1998) MindReader: querying databases through multiple examples. In: Proceedings of. VLDB conference, pp 218–227

  2. Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Video Technol 8(5):644

    Article  Google Scholar 

  3. Rui Y, Huang TS, Mehrotra S (1997) Content-based image retrieval with relevance feedback in MARS. In: Proceedings of international conference on image processing, pp 815–818

  4. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of ACM multimedia conference, pp 107–118

  5. Wu L, Faloutsos C, Sycara K, Payne TR (2000) FALCON: feedback adaptive loop for content-based retrieval. In: Proceedings of VLDB conference, pp 297–306

  6. Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems. MIT Press, Cambridge

    Google Scholar 

  7. Wu G, Chang EY, Panda N (2005) Formulating context-dependent similarity functions. In: Proceedings of ACM multimedia, pp 725–734

  8. Haykin S (1994) Neural networks: a comprehensive foundation. Maxmillan, New York

    MATH  Google Scholar 

  9. Schölkopf B, Kung S, Burges C, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765

    Article  Google Scholar 

  10. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  11. Hoi C-H, Lyu M (2004) A novel log-based relevance feedback technique in content-based image retrieval. In: Proceedings of ACM multimedia conference, pp 24–31

  12. Barnard K, Forsyth D (2003) Learning the semantics of words and pictures. J Mach Learn Res 3:1107–1135

    MATH  Google Scholar 

  13. Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of ACM SIGIR conference, pp 119–126

  14. Pan JY, Yang HJ, Duygulu P, Faloutsos C (2004) Automatic image captioning. In: Proceedings of IEEE international conference on multimedia and expo, pp 1987–1990

  15. Srikanth M, Varner J, Bowden M, Moldovan D (2005) Exploiting ontologies for automatic image annotation. In: Proceedings of ACM SIGIR conference, pp 552–558

  16. Chen G et al (2006) HISA: a query system bridging the semantic gap for large image databases. In: Proceedings of VLDB conference, pp 1187–1190

  17. He X, Ma W-Y, Zhang H-J (2004) Learning an image manifold for retrieval. In: Proceedings of ACM multimedia conference, pp 17–23

  18. Yu J, Guo Y, Tao D, Wan J (2015) Human pose recovery by supervised spectral embedding. Neurocomputing 166:301–308

    Article  Google Scholar 

  19. Yu J, Hong C (2017) Exemplar-based 3D human pose estimation with sparse spectral embedding. Neurocomputing 269:82–89

    Article  Google Scholar 

  20. Goh K-S, Li B, Chang E (2002) DynDex: a dynamic and non-metric space indexer. In: Proceedings of ACM multimedia, pp 466–475

  21. De Valois RL, De Valois KK (1988) Spatial vision. Oxford Science Publications, Oxford

    MATH  Google Scholar 

  22. Muneesawang P, Guan L (2004) An interactive approach for CBMR using a network of radial basis functions. IEEE Trans Multimed 6(5):703–716

    Article  Google Scholar 

  23. Ennis D (1991) Probabilistic models of perception. Dissertation, Wageningen Agricultual University, The Netherlands

  24. Shepard RN (1987) Toward a universal law of generalization for psychological science. Science 237(4820):1317–1323

    Article  MathSciNet  MATH  Google Scholar 

  25. Aggarwal CC (2018) Neural networks and deep learning. Springer, New York

    Book  MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03036561).

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  1. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea

    Guang-Ho Cha

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  1. Guang-Ho Cha
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Correspondence to Guang-Ho Cha.

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Cha, GH. Similarity ranking technique exploiting the structure of similarity relationships. Computing 105, 559–576 (2023). https://doi.org/10.1007/s00607-020-00859-w

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