Abstract
In many supervised learning problems, objects are represented as a sequence of observations. To classify such data, existing methods build classifiers either based on their dynamics, or the statistics of the observations. However, similar observations shared by most objects are uninformative for identification. In this paper, we present a new approach that identifies similar observations across objects and use only informative data for classification. To do this, we construct a weighted multipartite graph from the training data, with weights representing the similarities between observations from different objects. Identification of uninformative observations is modeled as clustering on this multipartite graph using a combinatorial optimization formulation. Two-level hierarchical classifiers are, then, built using the clustering results. The first layer of the classifiers associates the test observations with a certain cluster, whereas the second level identifies the object within the cluster. Data associated with uninformative clusters are screened out. Final identification for the group of observations is obtained using the majority voting rule only from the informative observations.
We apply our algorithm to the gait recognition problem. The hierarchical classifiers are built in four different feature spaces for silhouette images. Final classification is determined by aggregating results from these four feature spaces. The experimental results show that our method results in improved recognition rates in most cases compared with other previously reported methods.
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
Cheson, B.D.: Chronic Lymphoid Leukemias. Marcel Dekker, New York (2001)
Phillips, P., Sarkar, S., Robledo, I., Grother, P., Bowyer, K.: The gait identification challenge problem: Data sets and baseline algorithm. In: CVPR (2002)
Sundaresan, A., RoyChowdhury, A., Chellappa, R.: A hidden markov model based framework for recognition of humans from gait sequences. In: ICIP (2003)
Webb, A.: Statistical Pattern Recognition. Wiley, Chichester (2002)
Comon, P.: Independent component analysis, a new concept? Signal Processing 36, 287–314 (1994)
Dawande, M., Keskinocak, P., Swaminathan, J.M., Tayur, S.: On bipartite and multipartite clique problems. J of Algorithms 41, 388–403 (2001)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Oxford University Press Inc., Oxford (2001)
Hochbaum, D.S.: Approximating clique and biclique problems. J. of Algorithms 29, 174–200 (1997)
Cunado, D., Nixon, M., Carter, J.: Using gait as a biometric, via phase-weighted magnitude spectra. In: 1st Int. Conf. audio and video based biometric person authentification (1997)
Yam, C.Y., Nixon, M.S., Carter, J.N.: On the relationship of human walking and running: Automatic person identification by gait. In: ICPR (2002)
Tanawongsuwan, R., Bobick, A.: Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: CVPR, vol. II, pp. 726–731 (2001)
Niyogi, S., Adelson, E.: Analyzing and recognizing walking figures in xyt. In: CVPR (1994)
Zhang, R., Vogler, C., Metaxas, D.: Human gait recognition. In: IEEE Workshop on Articulated and Nonrigid Motion, in conjunction with CVPR 2004 (July 2004)
Collins, R., Gross, R., Shi, J.: Silhouette-based human identification from body shape and gait. In: International Conference on Automatic Face and Gesture Recognition (2002)
Murase, H., Sakai, R.: Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters 17, 155–162 (1996)
Huang, P., Harris, C., Nixon, M.: Human gait recognition in canonical space using temporal templates. IEE Proceedings - Vision, Image and Signal Processing 146, 93–100 (1999)
Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A., Chellappa, R.: Gait analysis for human identification. In: Proceedings of the 3rd International conference on Audio and Video Based Person Authentication (2003)
Little, L., Boyd, J.: Recognizing people by their gait: the shape of motion. Videre 1, 1–32 (1996)
Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE PAMI 25, 1505–1518 (2003)
Sunderesan, A., Chowdhury, A.K.R., Chellappa, R.: A hidden markov model based framework for recognition of humans from gait sequences. In: IEEE ICIP (2003)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 509–522 (2002)
Tou, J.T., Gonzalezn, R.C. (eds.): Pattern Recognition Principles. Addison-Wesley, Norwell (1974)
Vashist, A., Kulikowski, C., Muchnik, I.: Ortholog clustering on a multipartite graph. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS (LNBI), vol. 3692, pp. 328–340. Springer, Heidelberg (2005)
Genkin, A., Lewis, D.D., Madigan, D.: Large-scale bayesian logistic regression for text categorization. J. of Machine Learning (2004) (submitted)
Yager, R., Kacprzyk, J. (eds.): The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer Academic Publishers, Reading (1997)
Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE PAMI 20, 226–239 (1998)
Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The HumanID gait challenge problem: Data sets, performance, and analysis. IEEE PAMI 27, 162–177 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, R., Vashist, A., Muchnik, I., Kulikowski, C., Metaxas, D. (2005). A New Combinatorial Approach to Supervised Learning: Application to Gait Recognition. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_6
Download citation
DOI: https://doi.org/10.1007/11564386_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29229-6
Online ISBN: 978-3-540-32074-6
eBook Packages: Computer ScienceComputer Science (R0)