Abstract
This paper addresses the problem of statistically learning typical features which characterize object categories and particular features which characterize individual objects in the categories. For this purpose, we propose a probabilistic learning method of object categories and their composition based on a bag of feature representation of co-occurring segments of objects and their context. In this method, multi-class classifiers are learned based on intra-categorical probabilistic latent component analysis with variable number of classes and inter-categorical typicality analysis. Through experiments by using images of plural categories in an image database, it is shown that the method learns probabilistic structures which characterize not only object categories but also object composition of categories, especially typical and non-typical objects and context in each category.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bar, M.: Visual objects in context. Nature Reviews Neuroscience 5, 617–629 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Shlomo, G.: K-tree; a height balanced tree structured vector quantizer. In: Proc. of the 2000 IEEE Signal Processing Society Workshop. vol. 1, pp. 271–280 (2000)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42, 177–196 (2001)
Shashanka, M., Raj, B., Smaragdis, P.: Probabilistic latent variable models as nonnegative factorizations. Computational Intelligence and Neuroscience 2008 (2008)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their location in images. In: Proc. of IEEE ICCV, pp. 370–377 (2005)
Torralba, A.: Contextual priming for object detection. International Journal of Computer Vision 53, 169–191 (2003)
Rabinovich, A., Vedaldi, C., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: Proc. of IEEE ICCV (2007)
Galleguillos, C., Rabinovich, A., Belongie, S.: Object categorization using co-occurrence, location and appearance. In: Proc. of IEEE CS Conf. on CVPR (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atsumi, M. (2010). Learning Visual Object Categories and Their Composition Based on a Probabilistic Latent Variable Model. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)