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Multimedia Tools and Applications

, Volume 77, Issue 13, pp 16771–16793 | Cite as

Prior-based probabilistic latent semantic analysis for multimedia retrieval

  • Ruben Fernandez-Beltran
  • Filiberto Pla
Article
  • 120 Downloads

Abstract

Topic models have shown to be one of the most effective tools in Content-Based Multimedia Retrieval (CBMR). However, the high computational learning cost together with the huge expansion of multimedia collections limit the scalability of topic-based CBMR systems in real-life multimedia applications. The present work pursues a twofold objective. On the one hand, to study the effect of using clustering-based document reduction schemes over standard topic models pLSA (probabilistic Latent Semantic Analysis) and LDA (Latent Dirichlet Allocation). On the other hand, to develop a pLSA-based extension oriented to integrate this reduction scheme within the own model in order to improve the CBMR effectiveness. The experimental part of the work includes three different multimedia databases, three ranking functions, four retrieval scenarios, three different numbers of topics and ten document reduction levels. Experiments revealed that standard topic models are highly sensitive to the document reduction level whereas the proposed model is able to provide a competitive advantage within the content-based retrieval field.

Keywords

Information reduction Topic models Probabilistic latent semantic analysis Content-based multimedia retrieval 

Notes

Acknowledgements

This work was supported by the Spanish Ministry of Economy under the projects ESP2013-48458-C4-3-P and ESP2016-79503-C2-2-P, by Generalitat Valenciana through project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09.

References

  1. 1.
    Apt C, Damerau F, Weiss SM (1994) Automated learning of decision rules for text categorization. ACM Trans Inf Syst 12:233CrossRefGoogle Scholar
  2. 2.
    Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84CrossRefGoogle Scholar
  3. 3.
    Blei DM, Lafferty JD (2006) Dynamic topic models. In: ACM International conference on machine learningGoogle Scholar
  4. 4.
    Blei D, Ng A, Jordan M (2003) Latent dirichlet allocation. J Mach Learn Res 3(4-5):993–1022MATHGoogle Scholar
  5. 5.
    Bosch A, Zisserman A, Muñoz X (2006) Scene classification via plsa. In: European conference on computer vision, pp 517–530Google Scholar
  6. 6.
    Chang J, Gerrish S, Wang C, Boyd-graber JL, Blei DM (2009) Reading tea leaves: how humans interpret topic models. In: Advances in neural information processing systems 22, pp 288–296Google Scholar
  7. 7.
    Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Networks and Applications 19(2):171–209CrossRefGoogle Scholar
  8. 8.
    Duygulu P, Barnard K, Freitas JFGd, Forsyth DA (2002) Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: European Conference on Computer Vision, pp 97–112Google Scholar
  9. 9.
    Fahad A, Alshatri N, Tari Z, Alamri A, Khalil I, Zomaya AY, Foufou S, Bouras A (2014) A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing 2(3):267–279CrossRefGoogle Scholar
  10. 10.
    Feng D, Siu WC, Zhang HJ (2010) Multimedia information retrieval and management: technological fundamentals and applications. Springer publishing company inc, BerlinMATHGoogle Scholar
  11. 11.
    Fernandez-Beltran R, Pla F (2015) Incremental probabilistic latent semantic analysis for video retrieval. Image Vis Comput 38:1–12CrossRefGoogle Scholar
  12. 12.
    Fernandez-Beltran R, Pla F (2016) Latent topics-based relevance feedback for video retrieval. Pattern Recogn 51:72–84CrossRefGoogle Scholar
  13. 13.
    Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701CrossRefMATHGoogle Scholar
  14. 14.
    Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1-2):177–196CrossRefMATHGoogle Scholar
  15. 15.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70MathSciNetMATHGoogle Scholar
  16. 16.
    Hu P, Liu W, Jiang W, Yang Z (2014) Latent topic model for audio retrieval. Pattern Recogn 47(3):1138–1143CrossRefGoogle Scholar
  17. 17.
    Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recogn Lett 31(8):651–666CrossRefGoogle Scholar
  18. 18.
    Jiang YG, Ye G, Chang SF, Ellis D, Loui AC (2011) Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: ACM International conference on multimedia retrievalGoogle Scholar
  19. 19.
    Kakkonen T, Myller N, Sutinen E, Timonen J (2008) Comparison of dimension reduction methods for automated essay grading. Educ Technol Soc 11 (3):275–288Google Scholar
  20. 20.
    Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19CrossRefGoogle Scholar
  21. 21.
    Li AQ, Ahmed A, Ravi S, Smola AJ (2014) Reducing the sampling complexity of topic models. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 891–900Google Scholar
  22. 22.
    Lienhart R, Romberg S, Hörster E (2009) Multilayer plsa for multimodal image retrieval. In: ACM International conference on multimedia retrievalGoogle Scholar
  23. 23.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision.  https://doi.org/10.1109/ICCV.1999.790410, vol 2, pp 1150–1157
  24. 24.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110.  https://doi.org/10.1023/B:VISI.0000029664.99615.94 CrossRefGoogle Scholar
  25. 25.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  26. 26.
    Mikolajczyk K, Schmid C (2004) Scale &; affine invariant interest point detectors. Int J Comput Vis 60(1):63–86.  https://doi.org/10.1023/B:VISI.0000027790.02288.f2 CrossRefGoogle Scholar
  27. 27.
    Monay F, Gatica-Perez D (2007) Modeling semantic aspects for cross-media image indexing. IEEE Trans Pattern Anal Mach Intell 29(10):1802–1817CrossRefGoogle Scholar
  28. 28.
    Philip Chen CL, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347CrossRefGoogle Scholar
  29. 29.
    Rui YRY, Huang TS, Mehrotra S, Ortega M (1997) A relevance feedback architecture for content-based multimedia information retrieval systemsGoogle Scholar
  30. 30.
    Saxena A, Prasad M, Gupta A, Bharill N, Patel OP, Tiwari A, Er MJ, Ding W, Lin CT (2017) A review of clustering techniques and developments. Neurocomputing.  https://doi.org/10.1016/j.neucom.2017.06.053
  31. 31.
    Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: IEEE International conference on computer vision, vol 2, pp 1470–1477Google Scholar
  32. 32.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  33. 33.
    Sontag D, Roy D (2011) Complexity of inference in latent dirichlet allocation. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ (eds) Advances in neural information processing systems 24. Curran Associates Inc, Red Hook, pp 1008–1016Google Scholar
  34. 34.
    Than K, Ho TB (2012) Fully sparse topic models. In: European conference on machine learning, pp 490–505Google Scholar
  35. 35.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1 (6):80–83CrossRefGoogle Scholar
  36. 36.
    Yi X, Allan J (2009) A comparative study of utilizing topic models for information retrieval. In: European conference on IR research on advances in information retrievalGoogle Scholar
  37. 37.
    Yoshitaka A, Ichikawa T (1999) A survey on content-based retrieval for multimedia databases. IEEE Trans Knowl Data Eng 11(1):81–93CrossRefGoogle Scholar
  38. 38.
    Zeng J, Leng B, Xiong Z (2015) 3-d object retrieval using topic model. Multimedia Tools and Applications 74:7859–7881CrossRefGoogle Scholar
  39. 39.
    Zhai C (2008) Statistical language models for information retrieval a critical review. Found Trends Inf Retr 2(3):137–213CrossRefGoogle Scholar
  40. 40.
    Zhou X, Hu X, Zhang X (2007) Topic signature language models for ad hoc retrieval. IEEE Trans Knowl Data Eng 19(9):1276–1287CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellonSpain

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