Abstract
A new method for image object recognition is proposed. The complicated relation between the visual features and the recognizing result are modeled using evidence theory in the proposed method. Given a recognition task, new method constructs multiple SVMs each for a single feature, and then a modified combination rule is utilized to fuse initial results from multiple SVMs to a more reliable result as the initial results often conflict with each other. In this way, the influence of different features is tuned properly, thus the system may adapt itself to different recognition tasks. Experiments demonstrate the effectiveness of the proposed method.
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.
Similar content being viewed by others
References
Heisele, B., et al.: Face recognition: component-based versus global approaches. Computer Vision and Image Understanding 91, 6–21 (2003)
Pontil, M., Verri, A.: Object recognition with support vector machines. IEEE Trans. On PAMI 20, 637–646 (1998)
Chapelle, O., et al.: Choosing multiple parameters for support vector machines. Machine Learning 46(1), 131–159 (2002)
Kittler, J., et al.: Combining evidence in multimodal personal identity recognition systems. In: Int. Conf. on Audio- and Video-based Biometric Person Authentication, Crans Montana, Switzerland (1997)
Qian, F., et al.: SVM-Based Feature Weighting Method for Image Retrieval. In: Proceeding 4th ACM Intl Workshop on Multimedia Information Retrieval (2002)
Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999) (In Japanese, translation by Naoki Abe.)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61(1), 103–112 (2005)
Shafer, G.: Belief Function and Possibility Measure. Working Paper No. 163, School of Business, the University of Kansas, Lawrence (1984)
Li, B.C.: Effective Combination Rule of Evidence Theory. In: Proceeding of SPIE, vol. 4554, pp. 237–240 (2002)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Comaniciu, D., Meer, P.: Mean Shift Analysis and Applications. In: IEEE Int. Conf. Computer Vision (ICCV 1999), pp. 1197–1203 (1999)
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
Deng, Z., Li, B., Zhuang, J. (2005). Image Object Recognition by SVMs and Evidence Theory. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_59
Download citation
DOI: https://doi.org/10.1007/11526346_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27858-0
Online ISBN: 978-3-540-31678-7
eBook Packages: Computer ScienceComputer Science (R0)