Abstract
Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the uncertainty. Bayesian probabilistic approach is robust to manage the uncertainty, and powerful to model high-level contexts like the relationship between places and objects. In this paper, we propose a context-based Bayesian method with SIFT for scene understanding. At first, image pre-processing extracts features from vision information and objects-existence information is extracted by SIFT that is rotation and scale invariant. This information is provided to Bayesian networks for robust inference in scene understanding. Experiments in complex real environments show that the proposed method is useful.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Korpipaa, P., Koskinen, M., Peltola, J., Mäkelä, S., Seppänen, T.: Bayesian approach to sensor-based context awareness. Personal and Ubiquitous Computing Archive 7(4), 113–124 (2003)
Torralba, A., Mutphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: IEEE Int. Conf. Computer Vision, vol. 1(1), pp. 273–280 (2003)
Marengoni, M., Hanson, A., Zilberstein, S., Riseman, E.: Decision making and uncertainty management in a 3D reconstruction system. IEEE Trans. Pattern Analysis and Machine Intelligence 25(7), 852–858 (2003)
Luo, J., Savakis, A.E., Singhal, A.: A Bayesian network-based framework for semantic image understanding. Pattern Recognition 38(6), 919–934 (2005)
Strat, T.M., Fischler, M.A.: Context-based vision: Recognizing objects using information from both 2-D and 3-D imagery. IEEE Trans. Pattern Analysis and Machine Intelligence 13(10), 1050–1065 (1991)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Intl. J. Computer Vision 60(2), 91–110 (2004)
Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Understanding 96(2), 163–180 (2004)
Neapolitan, R.E.: Learning Bayesian Network. Prentice hall series in Artificial Intelligence (2003)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelets coefficients. Intl. J. Computer Vision 40(1), 49–71 (2000)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9(4), 309–347 (1992)
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
Im, SB., Cho, SB. (2006). Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_98
Download citation
DOI: https://doi.org/10.1007/11864349_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
eBook Packages: Computer ScienceComputer Science (R0)