Pattern Recognition and Image Analysis

, Volume 20, Issue 1, pp 21–28 | Cite as

Image classification via adaptive ensembles of descriptor-specific classifiers

  • T. FagniEmail author
  • F. Falchi
  • F. Sebastiani
Mathematical Methods in Pattern Recognition


An automated classification system usually consists of (i) a supervised learning algorithm for automatically generating classifiers from training data, and (ii) a representation scheme for converting the training objects into vectorial representations of their content. In this work, we take a detour from this tradition and present an approach to image classification based on an adaptive ensemble of classifiers, each specialized on classifying images based on a single “descriptor.” Each descriptor focuses on a different aspect, or perspective, of images; an ensemble of descriptor-specific classifiers can thus be seen as a committee of experts, each viewing the problem to be solved with a different slant, of from a different viewpoint. We test four different ways to set up such an ensemble, based on different ways of leveraging on the individual responses returned by each member of the ensemble, and on how confident these members are on their responses. We test this approach by using five different MPEG-7 descriptors on the task of assigning photographs of stone slabs to classes representing different types of stones. Our experimental results show important accuracy improvements with respect to a baseline in which a single classifier, working an all five descriptors at the same time, is employed.


image classification supervised learning classifier committees classifier ensembles metric spaces k-nearest neighbours classifier MPEG-7 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    G. Amato, F. Falchi, C. Gennaro, F. Rabitti, P. Savino, and P. Stanchev, “Improving Image Similarity Search Effectiveness in a Multimedia Content Management System,” in Proc. 10th Intern. Workshop on Multimedia Information System (MIS’04) (College Park, US, 2004), pp. 139–146.Google Scholar
  2. 2.
    E. Chávez, G. Navarro, R. Baeza-Yates, and J. L. Marroquín, “Searching in Metric Spaces,” ACM Comp. Surveys 33(3), 273–321 (2001).CrossRefGoogle Scholar
  3. 3.
    P. Ciaccia, M. Patella, and P. Zezula, “M-Tree: An Efficient Access Method for Similarity Search in Metric Spaces,” in Proc. 23rd Intern. Conf. on Very Large Data Bases (VLDB’97) (Athens, 1997), pp. 426–435.Google Scholar
  4. 4.
    L. Didaci and G. Giacinto, “Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule,” in Proc. 5th Intern. Workshop on Multiple Classifier Systems (MCS’04) (Cagliari, 2004), pp. 174–183.Google Scholar
  5. 5.
    T. G. Dietterich, “Ensemble Methods in Machine Learning,” in Proc. 1st Intern. Workshop on Multiple Classifier Systems (MCS’00) (Cagliari, 2000), pp. 1–15.Google Scholar
  6. 6.
    R. E. Schapire and Y. Singer, “Improved Boosting Using Confidence-rated Predictions,” Machine Learning 37(3), 297–336 (1999).zbMATHCrossRefGoogle Scholar
  7. 7.
    G. Giacinto and F. Roli, “Adaptive Selection of Image Classifiers,” in Proc. 9th Intern. Conf. on Image Analysis and Processing (ICIAP’97) (Firenze, 1997), pp. 38–45.Google Scholar
  8. 8.
    G. Giacinto and F. Roli, “Design of Effective Neural Network Ensembles for Image Classification Purposes,” Image and Vision Comp. 19, 699–707 (2001).CrossRefGoogle Scholar
  9. 9.
    T. Joachimes, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” in Proc. 10th Europ. Conf. on Machine Learning (ECML’98) (Chemnitz, 1998), pp. 137–142.Google Scholar
  10. 10.
    Y. H. Li and A. K. Jain, “Classification of Text Documents,” The Comp. J. 41(8), 537–546 (1998).zbMATHCrossRefGoogle Scholar
  11. 11.
    D. Lu and Q. Weng, “A Survey of Image Classification Methods and Techniques for Improving Classification Performance,” Int. J. Remote Sensing 28(5), 823–870 (2007).CrossRefGoogle Scholar
  12. 12.
    Introduction to MPEG-7: Multimedia Content Description Interface, Ed. by B. S. Manjunath, P. Salembier, and T. Sikora (John Wiley & Sons, New York, 2002).Google Scholar
  13. 13.
    J. Martínez-Alajarín, J. D. Luis-Delgado, and L. M. Thomás-Balibrea, “Automatic System for Quality-Based Classification of Marble Textures,” IEEE Transactions on Systems, Man, and Cybernetics — Part C: Applications and Review 35(4), 488–497 (2005).CrossRefGoogle Scholar
  14. 14.
    V. D. Mazurov, The Committee Method in Optimization and Classification Problems (Nauka, Moscow, 1990) [in Russian].Google Scholar
  15. 15.
    H. Samet, Foundations of Multidimensional and Metric Data Structures (Morgan Kaufmann, San Francisco, 2006).zbMATHGoogle Scholar
  16. 16.
    P. C. Smits, “Multiple Classifier System for Supervised Remote Sensing Image Classification Based on Dynamic Classifier Selection,” IEEE Transactions on Geoscience and Remote Sensing 40(4), 801–813 (2002).CrossRefGoogle Scholar
  17. 17.
    E. Spyrou, H. Le Borgne, T. Mailis, E. Cooke, Y. Avrithis, and N. O’Connor, “Fusing MPEG-7 Visual Descriptions for Image Classification,” in Proc. 15th Intern. Conf. on Artificial Neural Networks (ICANN’05) (Warsaw, 2005), pp. 847–852.Google Scholar
  18. 18.
    R. Takiyama, “A General Method for Training the Committee Machine,” Pattern Recognition 10(4), 255–259 (1978).zbMATHCrossRefGoogle Scholar
  19. 19.
    K. Woods, W. P. Kegelmeyer, Jr., and K. Bowyer, “Combination of Multiple Classifiers Using Local Accuracy Estimates,” IEEE Transactions on Pattern and Machine Intelligence 19(4), 405–410 (1997).CrossRefGoogle Scholar
  20. 20.
    L. Xu, A. Krzyzak, and C. Y. Suen, “Methods for Combing Multiple Classifiers and Their Applications to Handwriting Recognition,” IEEE Transactions on System, Man, and Cybernetics 22, 418–435 (1992).CrossRefGoogle Scholar
  21. 21.
    Yiming Yang, “An Evaluation of Statistical Approaches to Text Categorization,” Information Retrieval 1(1/2), 69–90 (1999).CrossRefGoogle Scholar
  22. 22.
    Yiming Yang and Xin Liu, “A Re-Examination of Text Categorization Methods,” in Proc. 22nd ACM Intern. Conf. on Research and Development in Informational Retrieval (SIGIR’99) (Berkeley, 1999), pp. 42–49.Google Scholar
  23. 23.
    Yiming Yang, Jian Zhang, and B. Kisiel, “A Scalability Analysis of Classifiers in Text Categorization,” in Proc. 26th ACM Intern. Conf. on Research and Development in Information Retrieval (SIGIR’03) (Toronto, 2003), pp. 96–103.Google Scholar
  24. 24.
    P. Zezula, G. Amato, V. Dohnal, and M. Batko, Similarity Search: The Metric Space Approach (Springer Verlag, Heidelberg, 2006).zbMATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2010

Authors and Affiliations

  1. 1.Istituto di Scienza e Tecnologia dell’ Informazione Consiglio Nazionale delle RicerchePisaItaly

Personalised recommendations