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Multimodal Object Recognition Using Random Clustering Trees

  • M. VillamizarEmail author
  • A. Garrell
  • A. Sanfeliu
  • F. Moreno-Noguer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)

Abstract

In this paper, we present an object recognition approach that in addition allows to discover intra-class modalities exhibiting high-correlated visual information. Unlike to more conventional approaches based on computing multiple specialized classifiers, the proposed approach combines a single classifier, Boosted Random Ferns (BRFs), with probabilistic Latent Semantic Analysis (pLSA) in order to recognize an object class and to find automatically the most prominent intra-class appearance modalities (clusters) through tree-structured visual words.

The proposed approach has been validated in synthetic and real experiments where we show that the method is able to recognize objects with multiple appearances.

Keywords

Object recognition Random trees Clustering Boosting 

Notes

Acknowledgments

Work partially supported by the Spanish Ministry of Science and Innovation under project DPI2013-42458-P, ERA-Net Chistera project ViSen PCIN-2013-047, and by the EU project ARCAS FP7-ICT-2011-28761.

References

  1. 1.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  2. 2.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  3. 3.
    Garrell, A., Villamizar, M., Moreno-Noguer, F., Sanfeliu, A.: Proactive behavior of an autonomous mobile robot for human-assisted learning. In: RO-MAN (2013)Google Scholar
  4. 4.
    Hinterstoisser, S., Lepetit, V., Fua, P., Navab, N.: Dominant orientation templates for real-time detection of texture-less objects. In: CVPR (2010)Google Scholar
  5. 5.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Kim, T.K., Cipolla, R.: Mcboost: multiple classifier boosting for perceptual co-clustering of images and visual features. In: NIPS, pp. 841–848 (2009)Google Scholar
  7. 7.
    Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. PAMI 28(9), 1465–1479 (2006)CrossRefGoogle Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. PAMI 32(3), 448–461 (2010)CrossRefGoogle Scholar
  10. 10.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)zbMATHCrossRefGoogle Scholar
  11. 11.
    Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: ICCV (2005)Google Scholar
  12. 12.
    Villamizar, M., Garrell, A., Sanfeliu, A., Moreno-Noguer, F.: Online human-assisted learning using random ferns. In: ICPR (2012)Google Scholar
  13. 13.
    Villamizar, M., Grabner, H., Andrade-Cetto, J., Sanfeliu, A., Van Gool, L., Moreno-Noguer, F.: Efficient 3D object detection using multiple pose-specific classifiers. In: BMVC (2011)Google Scholar
  14. 14.
    Villamizar, M., Sanfeliu, A., Andrade-Cetto, J.: Orientation invariant features for multiclass object recognition. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 655–664. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  15. 15.
    Villamizar, M., Sanfeliu, A., Moreno-Noguer, F.: Fast online learning and detection of natural landmarks for autonomous aerial robots. In: ICRA (2014)Google Scholar
  16. 16.
    Villamizar, M., Andrade-Cetto, J., Sanfeliu, A., Moreno-Noguer, F.: Bootstrapping boosted random ferns for discriminative and efficient object classification. Pattern Recognit. 45(9), 3141–3153 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • M. Villamizar
    • 1
    Email author
  • A. Garrell
    • 1
  • A. Sanfeliu
    • 1
  • F. Moreno-Noguer
    • 1
  1. 1.Institut de Robotica i Informatica Industrial CSIC-UPCBarcelonaSpain

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