Abstract
The goal of our work is object categorization in real-world scenes. That is, given a novel image we want to recognize and localize unseen-before objects based on their similarity to a learned object category. For use in a real-world system, it is important that this includes the ability to recognize objects at multiple scales.
In this paper, we present an approach to multi-scale object categorization using scale-invariant interest points and a scale-adaptive Mean-Shift search. The approach builds on the method from [12], which has been demonstrated to achieve excellent results for the single-scale case, and extends it to multiple scales. We present an experimental comparison of the influence of different interest point operators and quantitatively show the method’s robustness to large scale changes.
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
Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 113–127. Springer, Heidelberg (2002)
Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)
Collins, R.: Mean-shift blob tracking through scale space. In: CVPR 2003 (2003)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. Trans. PAMI 24(5), 603–619 (2002)
Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: ICCV 2001 (2001)
Dorko, G., Schmid, C.: Selection of scale invariant parts for object class recognition. In: ICCV 2003 (2003)
Fergus, R., Zisserman, A., Perona, P.: Object class recognition by unsupervised scaleinvariant learning. In: CVPR 2003 (2003)
Garg, A., Agarwal, S., Huang, T.: Fusion of global and local information for object detection. In: ICPR 2002 (2002)
Kadir, T., Brady, M.: Scale, saliency, and image description. IJCV 45(2), 83–105 (2001)
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV 2004 Workshop on Stat. Learn. in Comp. Vis. (2004)
Leibe, B., Schiele, B.: Interleaved object categorization and segmentation. In: BMVC 2003 (2003)
Lindeberg, T.: Feature detection with automatic scale selection. IJCV 30(2), 79–116 (1998)
Lowe, D.: Object recognition from local scale invariant features. In: ICCV 1999 (1999)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: ICCV 2001, pp. 525–531 (2001)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: CVPR 2003 (2003)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38(1) (2000)
Schneiderman, H., Kanade, T.: A statistical method of 3d object detection applied to faces and cars. In: CVPR 2000 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, pp. 511–518 (2001)
Weber, M., Welling, M., Perona, P.: Unsupervised learning of object models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leibe, B., Schiele, B. (2004). Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-28649-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
eBook Packages: Springer Book Archive