Abstract
In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
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
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60, 91–110 (2004)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Workshop on Stat. Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proc. of Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)
Fulkerson, B., Vedaldi, A., Soatto, S.: Localizing objects with smart dictionaries. In: Proc. of European Conference on Computer Vision, pp. 179–192 (2008)
Sastre, R., Tuytelaars, T., Bascon, S.: Class representative visual words for category-level object recognition. In: IbPRIA 2009: Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis, pp. 184–191 (2009)
Moosmann, F., Nowak, E., Jurie, F.: Randomized clustering forests for image classification. IEEE Trans. on Pat. Anal. and Machine Intel. 30, 1632–1646 (2008)
Shotton, J., Johnson, M., Cipolla, R., Center, T., Kawasaki, J.: Semantic texton forests for image categorization and segmentation. In: Proc. of Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Ramisa, A.: Localization and Object Recognition for Mobile Robots. PhD thesis, Universitat Autonoma de Barcelona (2009)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: Object localization by efficient subwindow search. In: Proc. of Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Proc. of European Conference on Computer Vision, pp. 490–503 (2006)
Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Trans. on Pat. Anal. and Machine Intel. 28(3), 416–431 (2006)
Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: Proc. of Computer Vision and Pattern Recognition, pp. 1491–1498 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. Int. Journal of Computer Vision 73, 213–238 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. Computer Vision and Image Understanding (CVIU) 110, 346–359 (2008)
Porikli, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: Proc. of Computer Vision and Pattern Recognition, pp. 829–836 (2005)
Marszalek, M., Schmid, C.: Accurate object localization with shape masks. In: Proc. of Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Lin, C.-J., Weng, R.C., Keerthi, S.S.: Trust region newton methods for large-scale logistic regression. In: Int. Conf. on Machine Learning, pp. 561–568 (2007)
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aldavert, D., Ramisa, A., Toledo, R., Lopez de Mantaras, R. (2009). Efficient Object Pixel-Level Categorization Using Bag of Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-10331-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10330-8
Online ISBN: 978-3-642-10331-5
eBook Packages: Computer ScienceComputer Science (R0)