Abstract
This paper presents a novel approach to the weak classifier selection based on the GentleBoost framework, based on sharing a set of features at each round. We explore the use of linear dimensionality reduction methods to guide the search for features that share some properties, such as correlations and discriminative properties. We add this feature set as a new parameter of the decision stump, which turns the single branch selection of the classic stump into a fuzzy decision that weights the contribution of both branches. The weights of each branch act as a confidence measure based on the feature set characteristics, which increases the accuracy and robustness to data perturbations. We propose an algorithm that consider the similarities between the weights provided by three linear mapping algorithms: PCA, LDA and MMLMNN [14]. We propose to analyze the row vectors of the linear mapping, grouping vector components with very similar values. Then, the created groups are the inputs of the FuzzyBoost algorithm. This search procedure generalizes the previous temporal FuzzyBoost [10] to any type of features. We present results in features with spatial support (images) and spatio-temporal support (videos), showing the generalization properties of the FuzzyBoost algorithm in other scenarios.
This work was supported by FCT (ISR/IST plurianual funding through the PIDDAC Program) and partially funded by EU Project First-MM (FP7-ICT-248258) and EU Project HANDLE (FP7-ICT-231640).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
CBCL face database #1, MIT center for biological and computation learning, http://cbcl.mit.edu/projects/cbcl/software-datasets/FaceData2.html
Avidan, S.: SpatialBoost: Adding spatial reasoning to adaBoost. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 386–396. Springer, Heidelberg (2006)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)
Figueira, D., Moreno, P., Bernardino, A., Gaspar, J., Santos-Victor, J.: Optical flow based detection in mixed human robot environments. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 223–232. Springer, Heidelberg (2009)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Jang, J.S.R.: Structure determination in fuzzy modeling: a fuzzy cart approach. In: Proceedings IEEE Conference on Fuzzy Systems, vol. 1, pp. 480–485 (June 1994)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28(1), 1–14 (1998)
Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138(2), 221–254 (2003)
Pla, F., Ribeiro, P.C., Santos-Victor, J., Bernardino, A.: Extracting motion features for visual human activity representation. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 537–544. Springer, Heidelberg (2005)
Ribeiro, P.C., Moreno, P., Santos-Victor, J.: Boosting with temporal consistent learners: An application to human activity recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part I. LNCS, vol. 4841, pp. 464–475. Springer, Heidelberg (2007)
Smith, P., da Vitoria Lobo, N., Shah, M.: Temporalboost for event recognition. In: International Conference on Computer Vision, vol. 1, pp. 733–740 (October 2005)
Suarez, A., Lutsko, J.F.: Globally optimal fuzzy decision trees for classification and regression. IEEE Transactions on PAMI 21(12), 1297–1311 (1999)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE PAMI 29(5), 854–869 (2007)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moreno, P., Ribeiro, P., Santos-Victor, J. (2011). Feature Set Search Space for FuzzyBoost Learning. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)