Abstract
We focus on learning graphical models of object classes from arbitrary instances of objects. Large intra-class variability of object appearance is dealt with by combining statistical local part detection with relations between object parts in a probabilistic network. Inference for view-based object recognition is done either with A ∗ -search employing a novel and dedicated admissible heuristic, or with Belief Propagation, depending on the network size.
Our approach is applicable to arbitrary object classes. We validate this for “faces” and for “articulated humans”. In the former case, our approach shows performance equal or superior to dedicated face recognition approaches. In the latter case, widely different poses and object appearances in front of cluttered backgrounds can be recognized.
This is a preview of subscription content, log in via an institution to check access.
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
Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: CVPR (2005)
Weber, M., Welling, M., Perona, P.: Unsupervised Learning of Models for Recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
Kumar, M.P., Torr, P.H.S., Zisserman, A.: Extending pictorial structures for object recognition. In: BMVC, pp. 789–798 (2004)
Gavrila, D., Philomin, V.: Real-time object detection using distance transforms. In: Proc. Intelligent Vehicles Conf. (1998)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV 61(1), 55–79 (2005)
Mikolajczyk, K., Schmid, C., Zisserman, A.: Human Detection Based on a Probabilistic Assembly of Robust Part Detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)
Ren, X., Berg, A., Malik, J.: Recovering human body configurations using pairwise constraints between parts. In: ICCV (2005)
Sigal, L., Isard, M., Sigelman, B., Black, M.: Attractive people: Assembling loose-limbed models using non-parametric belief propagation. In: NIPS (2003)
Triggs, B., Schmid, C., Ronfard, R.: Learning to Parse Pictures of People. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 700–714. Springer, Heidelberg (2002)
Ramanan, D., Forsyth, D.A., Zisserman, A.: Strike a pose: Tracking people by finding stylized poses. In: CVPR, vol. 1, pp. 271–278 (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their locations in images. In: ICCV. IEEE, Los Alamitos (2005)
Frey, B., Jojic, N.: A comparison of algorithms for inference and learning in probabilistic graphical models. IEEE PAMI 27(9), 1392–1416 (2005)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A Comparative Study of Energy Minimization Methods for Markov Random Fields. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 16–29. Springer, Heidelberg (2006)
Pham, T., Smeulders, A.: Object recognition with uncertain geometry and uncertain part detection. CVIU 99(2), 241–258 (2005)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE PAMI 26(2), 147–159 (2004)
Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (2000)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Trans. Information Theory 51(7), 2282–2312 (2005)
Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Tr. Syst. Sci. Cybernetics 4, 100–107 (1968)
Bergtholdt, M., Kappes, J., Schnörr: Graphical knowledge representation for human detection. In: International Workshop on The Representation and Use of Prior Knowledge in Vision (2006)
Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) Audio and Video based Person Authentication, pp. 90–95. Springer, Heidelberg (2001)
Cristinacce, D., Cootes, T.F., Scott, I.: A multi-stage approach to facial feature detection. In: BMVC (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bergtholdt, M., Kappes, J.H., Schnörr, C. (2006). Learning of Graphical Models and Efficient Inference for Object Class Recognition. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_28
Download citation
DOI: https://doi.org/10.1007/11861898_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
eBook Packages: Computer ScienceComputer Science (R0)