Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 128–143Cite as

A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting

  • Conference paper
  • First Online:

  • 2613 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

Abstract

Multiview representation of data is common in disciplines such as computer vision, bio-informatics, etc. Traditional fusion methods train independent classifiers on each view and finally conglomerate them using weighted summation. Such approaches are void from inter-view communications and thus do not guarantee to yield the best possible ensemble classifier on the given sample-view space. This paper proposes a new algorithm for multiclass classification using multi-view assisted supervised learning (MA-AdaBoost). MA-AdaBoost uses adaptive boosting for initially training baseline classifiers on each view. After each boosting round, the classifiers share their classification performances. Based on this communication, weight of an example is ascertained by its classification difficulties across all views. Two versions of MA-AdaBoost are proposed based on the nature of final output of baseline classifiers. Finally, decisions of baseline classifiers are agglomerated based on a novel algorithm of reward assignment. The paper then presents classification comparisons on benchmark UCI datasets and eye samples collected from FERET database. Kappa-error diversity diagrams are also studied. In majority instances, MA-AdaBoost outperforms traditional AdaBoost, variants of AdaBoost, and recent works on supervised collaborative learning with respect to convergence rate of training set and generalization errors. The error-diversity results are also encouraging.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14, 1612 (1999)

    Google Scholar 

  2. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. ICML 96, 148–156 (1996)

    Google Scholar 

  3. Zhang, H., Yang, Z., Gönen, M., Koskela, M., Laaksonen, J., Honkela, T., Oja, E.: Affective abstract image classification and retrieval using multiple kernel learning. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part III. LNCS, vol. 8228, pp. 166–175. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Masulli, F., Mitra, S.: Natural computing methods in bioinformatics: a survey. Inf. Fusion 10, 211–216 (2009)

    Article  Google Scholar 

  5. Duangsoithong, R., Windeatt, T.: Relevant and redundant feature analysis with ensemble classification. In: Seventh International Conference on Advances in Pattern Recognition. ICAPR 2009, pp. 247–250. IEEE (2009)

    Google Scholar 

  6. Culp, M., Michailidis, G., Johnson, K.: On multi-view learning with additive models. Ann. Appl. Stat. 3, 292–318 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  7. Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 287–294. ACM (1992)

    Google Scholar 

  8. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM (1998)

    Google Scholar 

  9. Zhu, X.: Semi-supervised learning. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 892–897. Springer, Heidelberg (2010)

    Google Scholar 

  10. Li, G., Chang, K., Hoi, S.C.: Multiview semi-supervised learning with consensus. IEEE Trans. Knowl. Data Eng. 24, 2040–2051 (2012)

    Article  Google Scholar 

  11. Cui, X., Huang, J., Chien, J.T.: Multi-view and multi-objective semi-supervised learning for hmm-based automatic speech recognition. IEEE Trans. Audio Speech Lang. Process. 20, 1923–1935 (2012)

    Article  Google Scholar 

  12. Wang, W., Zhou, Z.H.: On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1152–1159. ACM (2008)

    Google Scholar 

  13. Zhou, Z.H., Li, M.: Semi-supervised learning by disagreement. Knowl. Inf. Syst. 24, 415–439 (2010)

    Article  Google Scholar 

  14. Liu, W., Tao, D.: Multiview hessian regularization for image annotation. IEEE Trans. Image Process. 22, 2676–2687 (2013)

    Article  MathSciNet  Google Scholar 

  15. Hady, M., Schwenker, F.: Co-training by committee: a new semi-supervised learning framework. In: IEEE International Conference on Data Mining Workshops. ICDMW 2008, pp. 563–572. IEEE (2008)

    Google Scholar 

  16. Snoek, C.G., Worring, M., Smeulders, A.W.: Early versus late fusion in semantic video analysis. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 399–402. ACM (2005)

    Google Scholar 

  17. Liu, J., Li, J., Sun, X., Xie, Y., Lei, J., Hu, Q.: An embedded co-adaboost based construction of software document relation coupled resource spaces for cyber-physical society. Future Gener. Comput. Syst. 32, 198–210 (2014)

    Article  Google Scholar 

  18. Liu, J., Li, J., Xie, Y., Lei, J., Hu, Q.: An embedded co-adaboost and its application in classification of software document relation. In: 2012 Eighth International Conference on Semantics, Knowledge and Grids (SKG), pp. 173–180. IEEE (2012)

    Google Scholar 

  19. Koço, S., Capponi, C.: A boosting approach to multiview classification with cooperation. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 6912, pp. 209–228. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Koço, S., Capponi, C., Béchet, F.: Applying multiview learning algorithms to human-human conversation classification. In: INTERSPEECH (2012)

    Google Scholar 

  21. Philips, P., Wechsler, H., Huang, J., Rauss, P.: The feret database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16, 295–306 (1998)

    Article  Google Scholar 

  22. Philips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)

    Article  Google Scholar 

  23. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, pp. 555–562. IEEE (1998)

    Google Scholar 

  24. Seyedhosseini, M., Paiva, A.R., Tasdizen, T.: Fast adaboost training using weighted novelty selection. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1245–1250. IEEE (2011)

    Google Scholar 

  25. Ni, W., Huang, Y., Li, D., Wang, Y.: Boosting over groups and its application to acronym-expansion extraction. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 27–38. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  26. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  27. Bache, K., Lichman, M.: Uci machine learning repository (2013). http://archive.ics.uci.edu/ml

  28. Zhang, C.X., Zhang, J.S., Zhang, G.Y.: An efficient modified boosting method for solving classification problems. J. Comput. Appl. Math. 214, 381–392 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  29. Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619–1630 (2006)

    Article  Google Scholar 

  30. Margineantu, D.D., Dietterich, T.G.: Pruning adaptive boosting. ICML 97, 211–218 (1997). Citeseer

    Google Scholar 

  31. Mallah, C., Cope, J., Orwell, J.: Plant leaf classification using probabilistic integration of shape, texture and margin features. In: Computer Graphics and Imaging/798: Signal Processing, Pattern Recognition and Applications (CGIM2013), pp. 2013–798. Acta Press (2013)

    Google Scholar 

  32. Mallah, C., Cope, J.: Probabilistic classification from a k-nearest-neighbour classifier. Comput. Res. 1, 1–9 (2013)

    Google Scholar 

Download references

Acknowledgement

The work is financially supported by Space Technology Cell, ISRO, Ahmedabad under the “SVD” project. The work is dedicated to our respected Lt. Prof. Somnath Sengupta, Dept. of E&ECE, IIT Kharagpur.

Author information

Authors and Affiliations

  1. Indian Institute of Technology, Kharagpur, India

    Avisek Lahiri & Prabir Kumar Biswas

Authors
  1. Avisek Lahiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prabir Kumar Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avisek Lahiri .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Lahiri, A., Biswas, P.K. (2015). A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16811-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation