Advertisement

Multi-level Adaptive Active Learning for Scene Classification

  • Xin Li
  • Yuhong Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)

Abstract

Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent object-based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an “unexpected” target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.

Keywords

Active Learning Scene Classification 

References

  1. 1.
    Biswas, A., Parikh, D.: Simultaneous active learning of classifiers & attributes via relative feedback. In: Proceedings of CVPR (2013)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of CVPR (2005)Google Scholar
  3. 3.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Scene parsing with multiscale feature learning, purity trees,and optimal covers. CoRR abs/1202.2160 (2012)Google Scholar
  4. 4.
    Fei-Fei, P.L., Perona: A bayesian hierarchical model for learning natural scene categories. In: Proceedings of CVPR (2005)Google Scholar
  5. 5.
    Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: Proceedings of NIPS (2009)Google Scholar
  6. 6.
    Guo, Y., Greiner, R.: Optimistic active learning using mutual information. In: Proceedings of IJCAI (2007)Google Scholar
  7. 7.
    Jain, P., Kapoor, A.: Active learning for large multi-class problems. In: Proceedings of CVPR (2009)Google Scholar
  8. 8.
    Joshi, A., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: Proceedings of CVPR (2009)Google Scholar
  9. 9.
    Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Active learning with gaussian processes for object categorization. In: Proceedings of ICCV (2007)Google Scholar
  10. 10.
    A., Kapoor, G.H., Akbarzadeh, A., Baker, S.: Which faces to tag: Adding prior constraints into active learning. In: Proceedings of ICCV (2009)Google Scholar
  11. 11.
    Kovashka, A., Vijayanarasimhan, S., Grauman, K.: Actively selecting annotations among objects and attributes. In: Proceedings of ICCV (2011)Google Scholar
  12. 12.
    Kumar, M., Koller, D.: Efficiently selecting regions for scene understanding. In: Proceedings of CVPR (2010)Google Scholar
  13. 13.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR (2006)Google Scholar
  14. 14.
    Li, L., Su, H., Xing, E., Fei-Fei, L.: Object bank: A high-level image representation for scene classification & semantic feature sparsification. In: Proceedings of NIPS (2010)Google Scholar
  15. 15.
    Li, X., Guo, Y.: Adaptive active learning for image classification. In: Proceedings of CVPR (2013)Google Scholar
  16. 16.
    Lin, C., Weng, R., Keerthi, S.: Trust region newton method for logistic regression. J. Mach. Learn. Res. 9 (June 2008)Google Scholar
  17. 17.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2) (November 2004)Google Scholar
  18. 18.
    Mensink, T., Verbeek, J., Csurka, G.: Learning structured prediction models for interactive image labeling. In: Proceedings of CVPR (2011)Google Scholar
  19. 19.
    Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based models. In: Proceedings of ICCV (2011)Google Scholar
  20. 20.
    Parizi, S., Oberlin, J., Felzenszwalb, P.: Reconfigurable models for scene recognition. In: Proceedings of CVPR (2012)Google Scholar
  21. 21.
    Parkash, A., Parikh, D.: Attributes for classifier feedback. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 354–368. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Patterson, G., Hays, J.: Sun attribute database: Discovering, annotating, and recognizing scene attributes. In: Proceeding of CVPR (2012)Google Scholar
  23. 23.
    Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: Proceedings of CVPR (2009)Google Scholar
  24. 24.
    Sadeghi, F., Tappen, M.F.: Latent pyramidal regions for recognizing scenes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 228–241. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  25. 25.
    Settles, B.: Active Learning. Synthesis digital library of engineering and computer science. Morgan & Claypool (2011)Google Scholar
  26. 26.
    Sharmanska, V., Quadrianto, N., Lampert, C.H.: Augmented attribute representations. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 242–255. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  27. 27.
    Siddiquie, B., Gupta, A.: Beyond active noun tagging: Modeling contextual interactions for multi-class active learning. In: Proceedings of CVPR (2010)Google Scholar
  28. 28.
    Jones, K.S., Willett, P.: Readings in Information Retrieval. Morgan Kaufmann Publishers Inc. (1997)Google Scholar
  29. 29.
    Vezhnevets, A., Buhmann, J., Ferrari, V.: Active learning for semantic segmentation with expected change. In: Proceedings of CVPR (2012)Google Scholar
  30. 30.
    Vijayanarasimhan, S., Grauman, K.: Multi-level active prediction of useful image annotations for recognition. In: Proceedings of NIPS (2008)Google Scholar
  31. 31.
    Vijayanarasimhan, S., Grauman, K.: Large-scale live active learning: Training object detectors with crawled data and crowds. In: Proceedings of CVPR (2011)Google Scholar
  32. 32.
    Wang, Y., Mori, G.: A discriminative latent model of object classes and attributes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 155–168. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Wu, J., Rehg, J.: CENTRIST: A Visual Descriptor for Scene Categorization. IEEE Transactions on PAMI 33 (2011)Google Scholar
  34. 34.
    Yan, A.R., Yang, L., Hauptmann: Automatically labeling video data using multi-class active learning. In: Proceedings of ICCV (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xin Li
    • 1
  • Yuhong Guo
    • 1
  1. 1.Department of Computer and Information SciencesTemple UniversityPhiladelphiaUSA

Personalised recommendations