Environment-Adaptive Learning: How Clustering Helps to Obtain Good Training Data

  • Shoubhik Debnath
  • Shiv Sankar Baishya
  • Rudolph Triebel
  • Varun Dutt
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8736)


In this paper, we propose a method to combine unsupervised and semi-supervised learning (SSL) into a system that is able to adaptively learn objects in a given environment with very little user interaction. The main idea of our approach is that clustering methods can help to reduce the number of required label queries from user interaction, and at the same time provide the potential to select useful data to learn from. In contrast to standard methods, we train our classifier only on data from the actual environment and only if the clustering gives enough evidence that the data is relevant. We apply our method to the problem of object detection in indoor environments, for which we use a region-of-interest detector before learning. In experiments we show that our adaptive SSL method can outperform the standard non-adaptive supervised approach on an indoor office data set.


Semi-supervised learning active learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhu, X.: Semi-supervised learning literature survey. Computer Sciences, University of Wisconsin-Madison, Tech. Rep. 1530 (2005)Google Scholar
  2. 2.
    Zhu, X.: Semi-supervised learning with graphs. Ph.D. dissertation, Carnegie Mellon University (2005)Google Scholar
  3. 3.
    Lawrence, N.D., Platt, J.C., Jordan, M.I.: Extensions of the informative vector machine. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Machine Learning Workshop. LNCS (LNAI), vol. 3635, pp. 56–87. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Saffari, A., Leistner, C., Bischof, H.: Regularized multi-class semi-supervised boosting. In: Conf. on Comp. Vision & Patt. Recog., CVPR (2009)Google Scholar
  5. 5.
    Joachims, T.: Transductive inference for text classification using support vector machines, pp. 200–209 (1999)Google Scholar
  6. 6.
    Triebel, R., Paul, R., Rus, D., Newman, P.: Parsing outdoor scenes from streamed 3D laser data using online clustering and incremental belief updates. In: Robotics Track of AAAI Conference on Artificial Intelligence (2012)Google Scholar
  7. 7.
    Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: Conf. on Comp. Vision & Patt. Recog., CVPR (2010)Google Scholar
  8. 8.
    Ebert, S., Larlus, D., Schiele, B.: Extracting structures in image collections for object recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 720–733. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Budvytis, I., Badrinarayanan, V., Cipolla, R.: Semi-supervised video segmentation using tree structured graphical models. Trans. on Pattern Analysis and Machine Intelligence 35(11), 2751–2764 (2013)CrossRefGoogle Scholar
  10. 10.
    Settles, B.: Active learning literature survey. Tech. Rep. (2010)Google Scholar
  11. 11.
    Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. Intern. Journal of Computer Vision 88(2), 169–188 (2010)CrossRefGoogle Scholar
  12. 12.
    Triebel, R., Grimmett, H., Paul, R., Posner, I.: Driven learning for driving: How introspection improves semantic mapping. In: The International Symposium on Robotics Research, ISRR (2013)Google Scholar
  13. 13.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of the Intern. Conf. on Computer Vision (ICCV), pp. 1150–1157 (1999)Google Scholar
  14. 14.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. Trans. on Pattern Analysis and Machine Intelligence 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  15. 15.
    Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proc. of the Joint Conf. on Empirical Methods in Natural Language Proc. and Comp. Natural Language Learning (EMNLP-CoNLL), pp. 410–420 (2007)Google Scholar
  16. 16.
    Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Bo, L., Ren, X., Fox, D.: Hierarchical matching pursuit for image classification: Architecture and fast algorithms. In: NIPS (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shoubhik Debnath
    • 1
    • 2
  • Shiv Sankar Baishya
    • 1
    • 2
  • Rudolph Triebel
    • 1
  • Varun Dutt
    • 2
  • Daniel Cremers
    • 1
  1. 1.Computer Vision GroupTechnical University MunichGermany
  2. 2.Indian Institute of Technology MandiIndia

Personalised recommendations