Abstract
Methods for the recognition of multiple objects in images using local representations are introduced. Starting from a straight forward approach, we combine the use of local representations with region segmentation and template matching. The performance of the classifiers is evaluated on four image databases of different difficulties. All databases consist of images containing one, two or three objects and differ in the backgrounds which are used. Also, the presence or absence of occlusions of the objects in the scenes is considered. Classification results are promising regarding the difficulty of the task.
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
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM 45(6), 891–923 (1998)
Burkhardt, H., Siggelkow, S.: Invariant features in pattern recognition – fundamentals and applications. In: Kotropoulos, C., Pitas, I. (eds.) Nonlinear Model-Based Image/Video Processing and Analysis, pp. 269–307. Wiley, Chichester (2001)
Duin, R.P., Kittler, J., Hatef, M., Matas, J.: On combinig classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Frey, B.J., Jojic, N.: Transformation-invariant clustering using the EM algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 25(1), 1–17 (2003)
Keysers, D., Dahmen, J., Ney, H., Wein, B., Lehmann, T.: Statistical framework for model-based image retrieval in medical applications. Journal of Electronic Imaging 12(1), 59–69 (2003)
Keysers, D., Motter, M., Deselaers, T., Ney, H.: Training and recognition of complex scenes using a holistic statistical model. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 52–59. Springer, Heidelberg (2003)
Keysers, D., Paredes, R., Ney, H., Vidal, E.: Combination of tangent vectors and local representations for handwritten digit recognition. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 538–547. Springer, Heidelberg (2002)
Nene, S., Nayar, S., Murase, H.: Columbia object image library: COIL-100. Technical Report CUCS-006-96, Department of Computer Science, Columbia University, New York (February 1996)
Paredes, R., Keysers, D., Lehmann, T., Wein, B., Ney, H., Vidal, E.: Classification of medical images using local representations. In: Bildverarbeitung für die Medizin, Leipzig, Germany, March 2002, pp. 171–174 (2002)
Paredes, R., Perez-Cortes, J., Juan, A., Vidal, E.: Local representations and a direct voting scheme for face recognition. In: Workshop on Pattern Recognition in Information Systems, Setübal, Portugal, July 2001, pp. 71–79 (2001)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)
Shyu, C., Brodley, C.E., Kak, A.C., Kosaka, A., Aisen, A., Broderick, L.: Local versus global features for content-based image retrieval. In: Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, June 1998, pp. 30–34 (1998)
Smeulders, A.W.M., Worring, M., Santint, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deselaers, T., Keysers, D., Paredes, R., Vidal, E., Ney, H. (2003). Local Representations for Multi-object Recognition. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-45243-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40861-1
Online ISBN: 978-3-540-45243-0
eBook Packages: Springer Book Archive