Bayesian Online Learning of Corresponding Points of Objects with Sequential Monte Carlo

Authors: Miika Toivanen, Jouko Lampinen

Abstract:

This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images.

Keywords: Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079224

References:

[1] L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," IEEE TPAMI, vol. 19, pp. 775-779, 1997.
[2] T. Cootes, G. Edwards, and C. Taylor, "Active appearance models," IEEE TPAMI, vol. 23, no. 6, pp. 681-685, 2001.
[3] T. Tamminen and J. Lampinen, "Sequential Monte Carlo for Bayesian matching of objects with occlusions," IEEE TPAMI, vol. 28, pp. 930- 941, 2006.
[4] J. Kamarainen, M. Hamouz, J. Kittler, P. Paalanen, J. Ilonen, and A. Drobchenko, "Object localisation using generative probability model for spatial constellation and local image features," in Proc. ICCV, 2007, pp. 1-8.
[5] C. Doucet, J. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, 2001.
[6] M. Weber, M. Welling, and P. Perona, "Unsupervised learning of models for recognition," in Proc. ECCV, 2000, pp. 18-32.
[7] R. Fergus, P. Perona, and A. Zisserman, "Object class recognition by unsupervised scale-invariant learning," in Proc. CVPR, 2003, pp. 264- 271.
[8] L. Fei-Fei, R. Fergus, and P. Perona, "A Bayesian approach to unsupervised one-shot learning of object categories," in Proc. ICCV, 2003, pp. 1134-1141.
[9] K. Mikolajczyk, B. Leibe, and B. Schiele, "Multiple object class detection with a generative model," in Proc. CVPR, 2006, pp. 26-36.
[10] S. Lazebnik, C. Schmid, and J. Ponce, "A discriminative framework for texture and object recognition using local image features," Lecture notes in computer science, vol. 4170, p. 423, 2006.
[11] R. Fergus, P. Perona, and A. Zisserman, "A sparse object category model for efficient learning and complete recognition," in Toward Category- Level Object Recognition, ser. LNCS. Springer, 2007, vol. 4170, pp. 443-461.
[12] B. Leibe, A. Leonardis, and B. Schiele, "Combined object categorization and segmentation with an implicit shape model," Workshop on Statistical Learning in Computer Vision, ECCV, pp. 17-32, 2004.
[13] E. Borenstein, E. Sharon, and S. Ullman, "Combining top-down and bottom-up segmentation," in Proc. CVPR Workshop, 2004, pp. 46-53.
[14] J. Winn and N. Jojic, "Locus: Learning object classes with unsupervised segmentation," in Proc. ICCV, vol. 1, 2005.
[15] N. Ahuja and S. Todorovic, "Learning the taxonomy and models of categories present in arbitrary images," in Proc. ICCV, 2007, pp. 1-8.
[16] L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories," Computer Vision and Image Understanding, vol. 106, no. 1, pp. 59-70, 2007.
[17] J. Daugman, "Complete discrete 2-D Gabor transforms by neural networks for imageanalysis and compression," IEEE Transactions on Acoustics, Speech, and Signal Processing
[see also IEEE Transactions on Signal Processing], vol. 36, no. 7, pp. 1169-1179, 1988.
[18] R. Neal, "Probabilistic inference using Markov chain Monte Carlo methods," Department of Computer Science, University of Toronto, Tech. Rep., 1993.
[19] M. B. Stegmann, "Analysis and segmentation of face images using point annotations and linear subspace techniques," Informatics and Mathematical Modelling, Technical University of Denmark, Tech. Rep., 2002.