Adaptive Committees of Feature-specific Classifiers for Image Classification

Tiziano Fagni, Fabrizio Falchi, Fabrizio Sebastiani

Abstract

We present a system for image classification based on an adaptive committee of five classifiers, each specialized on classifying images based on a single MPEG-7 feature. We test four different ways to set up such a committee, and obtain important accuracy improvements with respect to a baseline in which a single classifier, working an all five features at the same time, is employed.

References

  1. Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5) (2007) 823-870
  2. Giacinto, G., Roli, F.: Adaptive selection of image classifiers. In: Proceedings of the 9th International Conference on Image Analysis and Processing (ICIAP'97), Firenze, IT (1997) 38-45
  3. Li, Y.H., Jain, A.K.: Classification of text documents. The Computer Journal 41(8) (1998) 537-546
  4. Woods, K., Kegelmeyer Jr, W., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern and Machine Intelligence 19(4) (1997) 405-410
  5. Schapire, R.E., Singer, Y.: Improved boosting using confidence-rated predictions. Machine Learning 37(3) (1999) 297-336
  6. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning (ECML'98), Chemnitz, DE (1998) 137-142
  7. Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 1(1/2) (1999) 69-90
  8. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd ACM International Conference on Research and Development in Information Retrieval (SIGIR'99), Berkeley, US (1999) 42-49
  9. Yang, Y., Zhang, J., Kisiel, B.: A scalability analysis of classifiers in text categorization. In: Proceedings of the 26th ACM International Conference on Research and Development in Information Retrieval (SIGIR'03), Toronto, CA (2003) 96-103
  10. Ch·vez, E., Navarro, G., Baeza-Yates, R., MarroquÙn, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3) (2001) 273-321
  11. Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco, US (2006)
  12. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer Verlag, Heidelberg, DE (2006)
  13. Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB 7897), Athens, GR (1997) 426-435
  14. Manjunath, B., Salembier, P., Sikora, T., eds.: Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, New York, US (2002)
  15. Amato, G., Falchi, F., Gennaro, C., Rabitti, F., Savino, P., Stanchev, P.: Improving image similarity search effectiveness in a multimedia content management system. In: Proceedings of the 10th International Workshop on Multimedia Information System (MIS'04), College Park, US (2004) 139-146
Download


Paper Citation


in Harvard Style

Fagni T., Falchi F. and Sebastiani F. (2009). Adaptive Committees of Feature-specific Classifiers for Image Classification . In Proceedings of the 2nd International Workshop on Image Mining Theory and Applications - Volume 1: Workshop IMTA, (VISIGRAPP 2009) ISBN 978-989-8111-80-7, pages 113-122. DOI: 10.5220/0001968501130122


in Bibtex Style

@conference{workshop imta09,
author={Tiziano Fagni and Fabrizio Falchi and Fabrizio Sebastiani},
title={Adaptive Committees of Feature-specific Classifiers for Image Classification},
booktitle={Proceedings of the 2nd International Workshop on Image Mining Theory and Applications - Volume 1: Workshop IMTA, (VISIGRAPP 2009)},
year={2009},
pages={113-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001968501130122},
isbn={978-989-8111-80-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Image Mining Theory and Applications - Volume 1: Workshop IMTA, (VISIGRAPP 2009)
TI - Adaptive Committees of Feature-specific Classifiers for Image Classification
SN - 978-989-8111-80-7
AU - Fagni T.
AU - Falchi F.
AU - Sebastiani F.
PY - 2009
SP - 113
EP - 122
DO - 10.5220/0001968501130122