Classification of Biological Objects Using Active Appearance Modelling and Color Cooccurrence Matrices

  • Anders Bjorholm Dahl
  • Henrik Aanæs
  • Rasmus Larsen
  • Bjarne K. Ersbøll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


We use the popular active appearance models (AAM) for extracting discriminative features from images of biological objects. The relevant discriminative features are combined principal component (PCA) vectors from the AAM and texture features from cooccurrence matrices. Texture features are extracted by extending the AAM’s with a textural warp guided by the AAM shape. Based on this, texture cooccurrence features are calculated. We use the different features for classifying the biological objects to species using standard classifiers, and we show that even though the objects are highly variant, the AAM’s are well suited for extracting relevant features, thus obtaining good classification results. Classification is conducted on two real data sets, one containing various vegetables and one containing different species of wood logs.


Face Recognition Object Recognition Biological Object Discriminative Feature Canonical Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  2. 2.
    Carstensen, J.M.: Description and Simulation of Visual Texture. PhD thesis, Institure of Mahematical Statistics and Operations Research, Technical University of Denmark, Kgs. Lyngby (1992)Google Scholar
  3. 3.
    Chang, P., Krumm, J.: Object Recognition with Color Cooccurrence Histogram (1999)Google Scholar
  4. 4.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Proc. SPIE Medical Imaging (2004)Google Scholar
  5. 5.
    Demirci, M., Shokoufandeh, A., Dickinson, S., Keselman, Y., Bretzner, L.: Manyto -many feature matching using spherical coding of directed graphs (2004)Google Scholar
  6. 6.
    Dickinson, S., Bretzner, L., Keselman, Y., Shokoufandeh, A., Demirci, M.F.: Object recognition as many-to-many feature matching. International Journal of Computer Vision 69(2), 203–222 (2006)CrossRefGoogle Scholar
  7. 7.
    Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face recognition using active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 581–595. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32(2), 407–451 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Fagertun, J., Gomez, D.D., Ersbøll, B.K., Larsen, R.: A face recognition algorithm based on multiple individual discriminative models. In: Olsen, S.I. (ed.) Dansk Selskab for Genkendelse af Mønstre (Danish Pattern Recognition Society) DSAGM 2005, DIKU Technical report 2005/06, pp. 69–75. Universitetsparken 1, København Ø, DIKU, University of Copenhagen (Aug. 2005)Google Scholar
  10. 10.
    Hansen, D.W., Nielsen, M., Hansen, J.P., Johansen, A.S., Stegmann, M.B.: Tracking eyes using shape and appearance. In: IAPR Workshop on Machine Vision Applications - MVA, Dec. 2002, pp. 201–204 (2002)Google Scholar
  11. 11.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  12. 12.
    Hastie, T., Tibshirani, J., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  15. 15.
    Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recognition 37(5), 965–976 (2004)CrossRefGoogle Scholar
  16. 16.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)CrossRefGoogle Scholar
  17. 17.
    Skoglund, K.: The lars-en algorithm for elastic net regression - matlab implementation, (2006)Google Scholar
  18. 18.
    Stegmann, M.B.: Object tracking using active appearance models. In: Olsen, S.I. (ed.) Proc. 10th Danish Conference on Pattern Recognition and Image Analysis, vol. 1, Copenhagen, Denmark, Jul. 2001, pp. 54–60. DIKU (2001)Google Scholar
  19. 19.
    Stegmann, M.B., Ersbøll, B.K., Larsen, R.: FAME - a flexible appearance modelling environment. IEEE Transactions on Medical Imaging 22(10), 1319–1331 (2003)CrossRefGoogle Scholar
  20. 20.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ’91, pp. 586–591 (1991)Google Scholar
  21. 21.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B, Statistical Methodology 67(2), 301 (2005)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Anders Bjorholm Dahl
    • 1
    • 2
  • Henrik Aanæs
    • 1
  • Rasmus Larsen
    • 1
  • Bjarne K. Ersbøll
    • 1
  1. 1.Informatics and Mathematical Modelling, Technical University of Denmark 
  2. 2.Dralle A/S - Cognitive Systems, CopenhagenDenmark

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