Latent Topic Encoding for Content-Based Retrieval

  • Ruben Fernandez-Beltran
  • Filiberto Pla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


This work presents a new encoding approach based on latent topics which is specially designed to Content-Based Retrieval tasks. The novelty of the proposed Latent Topic Encoding (LTE) lies in two points: (1) defining the visual vocabulary according to the hidden patterns discovered from the local descriptors; and (2) encoding each sample by accumulating the proportion of its local features over topics. Several retrieval simulations using two different databases have been carried out to test the performance of the proposed approach with respect to the standard visual Bag of Words (BoW). Results show that LTE encoding is able to outperform the traditional visual BoW when the retrieval task is performed in the latent topic space.


Encoding Visual Bag-of-Words Topic modelling Content-based retrieval 


  1. 1.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM TMCCA 2(1), 1–19 (2006)Google Scholar
  2. 2.
    Ren, W., Singh, S., Singh, M., Zhu, Y.S.: State-of-the-art on spatio-temporal information-based video retrieval. Pattern Recogn. 42(2), 267–282 (2009)zbMATHCrossRefGoogle Scholar
  3. 3.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE TPAMI 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  4. 4.
    Zhou, X., Huang, T.: Relevance feedback in image retrieval: a comprehensive review. Multimedia Syst. 8(6), 536–544 (2003)CrossRefGoogle Scholar
  5. 5.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: ICCV, vol. 2, pp. 1470–1477 (2003)Google Scholar
  7. 7.
    Li, F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)Google Scholar
  8. 8.
    Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: improving particular object retrieval in large scale image databases. In: CVPR (2008)Google Scholar
  9. 9.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong. Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)Google Scholar
  10. 10.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  11. 11.
    Zhou, X., Yu, K., Zhang, T., Huang, T.S.: Image classification using super-vector coding of local image descriptors. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 141–154. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  12. 12.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classificationv via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  13. 13.
    Fernández-Beltran, R., Pla, F.: An interactive video retrieval approach based on latent topics. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 290–299. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  14. 14.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)zbMATHGoogle Scholar
  15. 15.
    Saleh,B., Farhadi, A., Elgammal. A.: Object-centric anomaly detection by attribute-based reasoning. In: CVPR, pp. 787–794 (2013)Google Scholar
  16. 16.
    Jiang, Y.G.,Ye, G., Chang, S.F., Ellis, D., Loui, A.C.: Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: ACM ICMR (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellónSpain

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