This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Generic Object Recognition with Boosting
March 2006 (vol. 28 no. 3)
pp. 416-431
This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of either discontinuity or homogeneity. A variety of local descriptors can be applied to form a set of feature vectors for each local region. Boosting is used to learn a subset of such feature vectors (weak hypotheses) and to combine them into one final hypothesis for each visual category. This combination of individual extractors and descriptors leads to recognition rates that are superior to other approaches which use only one specific extractor/descriptor setting. To explore the limitation of our system, we had to set up new, highly complex image databases that show the objects of interest at varying scales and poses, in cluttered background, and under considerable occlusion. We obtain classification results up to 81 percent ROC-equal error rate on the most complex of our databases. Our approach outperforms all comparable solutions on common databases.

[1] S. Agarwal and D. Roth, “Learning a Sparse Representation for Object Detection,” Proc. European Conf. Computer Vision, pp. 113-130, 2002.
[2] K. Barnard, P. Duygulu, R. Guru, P. Gabbur, and D. Forsyth, “The Effects of Segmentation and Feature Choice in a Translation Model of Object Recognition,” Proc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 675-682, 2003.
[3] P. Carbonetto, N. de Freitas, and K. Barnard, “A Statistical Model for General Contextual Object Recognition,” Proc. European Conf. Computer Vision, pp. 350-362, 2004.
[4] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1026-1038, Aug. 2002.
[5] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[6] G.Y. Dorko and C. Schmid, “Selection of Scale-Invariant Parts for Object Class Recognition,” Proc. Int'l Conf. Computer Vision, pp. 634-640, 2003.
[7] P. Felzenszwalb and D. Huttenlocher, “Pictorial Structures for Object Recognition” Int'l J. Computer Vision, vol. 61, no. 1, pp. 55-79, 2004.
[8] R. Fergus, P. Perona, and A. Zisserman, “Object Class Recognition by Unsupervised Scale-Invariant Learning,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 264-272, 2003.
[9] R. Fergus, P. Perona, and A. Zisserman, “A Visual Category Filter for Google Images,” Proc. European Conf. Computer Vision, pp. 242-256, 2004.
[10] V. Ferrari, T. Tuytelaars, and L. Van Gool, “Simultaneous Object Recognition and Segmentation by Image Exploration,” Proc. European Conf. Computer Vision, pp. 40-54, 2004.
[11] W. Freeman and E. Adelson, “The Design and Use of Steerable Filters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906 Sept. 1991.
[12] Y. Freund and R. Schapire, “A Decision Theoretic Generalisation of Online Learning,” Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
[13] M. Fussenegger, A. Opelt, A. Pinz, and P. Auer, “Object Recognition Using Segmentation for Feature Detection,” Proc. Int'l Conf. Pattern Recognition, 2004.
[14] R.C. Gonzalez and R.E. Woods, Digital Image Processing. Addison-Wesley, 2001.
[15] L. Van Gool, T. Moons, and D. Ungureanu, “Affine/Photometric Invariants for Planar Intensity Patterns,” Proc. European Conf. Computer Vision, pp. 642-651, 1996.
[16] R.M. Haralick, “Statistical and Structural Approaches to Texture,” Proc. IEEE, vol. 67, pp. 786-804, 1979.
[17] C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proc. Fourth Alvey Vision Conf., pp. 189-192, 1988.
[18] R. Laganiere, “A Morphological Operator for Corner Detection,” Pattern Recognition, vol. 31, no. 11, pp. 1643-1652, 1998.
[19] Y. LeCun, F.J. Huang, and L. Bottou, “Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting,” Proc. Conf. Computer Vision and Pattern Recognition, 2004.
[20] B. Leibe, A. Leonardis, and B. Schiele, “Combined Object Categorization and Segmentation with an Implicit Shape Model,” Proc. European Conf. Computer Vision Workshop Statistical Learning in Computer Vision, May 2004.
[21] T.K. Leung, M.C. Burl, and P. Perona, “Probabilistic Affine Invariants for Recognition,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 678-684, June 1998.
[22] T. Lindeberg, “Feature Detection with Automatic Scale Selection,” Int'l J. Computer Vision, vol. 30, no. 2, pp. 79-116, 1998.
[23] D.G. Lowe, “Object Recognition from Local Scale-Invariant Features,” Proc. Int'l Conf. Computer Vision, pp. 1150-1157, 1999.
[24] W. Maass and M. Warmuth, “Efficient Learning with Virtual Threshold Gates,” Information and Computation, vol. 141, no. 1, pp. 66-83, 1998.
[25] S. Maitra, “Moment Invariants,” Proc. IEEE, pp. 679-699, 1979.
[26] K. Mikolajczyk and C. Schmid, “Indexing Based on Scale Invariant Interest Points,” Proc. Int'l Conf. Computer Vision, pp. 525-531, 2001.
[27] K. Mikolajczyk and C. Schmid, “An Affine Invariant Interest Point Detector,” Proc. European Conf. Computer Vision, pp. 128-142, 2002.
[28] K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” Proc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 257-263, 2003.
[29] A. Opelt, “Feature Selection for Scaled Interest Points,” master's thesis, Graz Univ. of Tech nology, 2003.
[30] A. Opelt, M. Fussenegger, A. Pinz, and P. Auer, “Weak Hypotheses and Boosting for Generic Object Detection and Recognition,” Proc. European Conf. Computer Vision, vol. 2, pp. 71-84, 2004.
[31] C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of Interest Point Detectors,” Int'l J. Computer Vision, vol. 37, no. 2, pp. 151-177, 2004.
[32] C. Schmid and R. Mohr, “Local Grayvalue Invariants for Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530-535, May 1997.
[33] H. Schneiderman and T. Kanade, “Object Detection Using the Statistics of Parts,” Int'l J. Computer Vision, vol. 56, no. 3, pp. 151-177, 2004.
[34] A. Selinger and R.C. Nelson, “Improving Appearance-Based Object Recognition in Cluttered Background,” Proc. Int'l Conf. Pattern Recognition, vol. 1, pp. 1-8, 2000.
[35] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[36] E. Shilat, M. Werman, and Y. Gdalyahu, “Ridge's Corner Detection and Correspondence,” Proc. Computer Vision and Pattern Recognition, pp. 976-981, 1997.
[37] J. Thureson and S. Carlsson, “Appearance Based Qualitative Image Description for Object Class Recognition,” Proc. European Conf. Computer Vision, pp. 518-529, 2004.
[38] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[39] P. Viola, M. Jones, and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” Proc. Int'l Conf. Computer Vision, vol. 2, pp. 734-741, 2003.
[40] C. Wallraven, B. Caputo, and A. Graf, “Recognition with Local Features: The Kernel Recipe,” Proc. Int'l Conf. Computer Vision, pp. 257-264, 2003.
[41] M. Weber, M. Welling, and P. Perona, “Unsupervised Learning of Models for Recognition,” Proc. European Conf. Computer Vision, 2000.
[42] R.P. Wuertz and T. Lourens, “Corner Detection in Color Images by Multiscale Combination of End-Stopped Cortical Cells,” Proc. Int'l Conf. Artificial Neuronal Networks, pp. 901-906, 1997.

Index Terms:
Boosting, object categorization, object localization.
Citation:
Andreas Opelt, Axel Pinz, Michael Fussenegger, Peter Auer, "Generic Object Recognition with Boosting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 416-431, March 2006, doi:10.1109/TPAMI.2006.54
Usage of this product signifies your acceptance of the Terms of Use.