Abstract
Object detection in real images has attracted much attention during the last decade. Using machine learning and large databases it is possible to develop detectors for visual categories that have a very high hit-rate, with low false positive rates. In this paper we investigate a general probabilistic framework for context based scene interpretation using multiple detectors. Methods for finding maximum likelihood estimates of scenes given detection results are presented. Although we have investigated how the method works for a specific case, namely for face detection, it is a general method. We show how to combine the results of a number of detectors i.e. face, eye, nose and mouth detectors. The methods have been tested using detectors trained on real images, with promising results.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: Proc. Conf. Computer Vision and Pattern Recognition, San Diego, USA, pp. I: 10–17 (2005)
Dryden, I.L., Mardia, K.V., Walder, A.N.: Review of the use of context in statistical image analysis. J. of Applied Statistics 24(5), 513–538 (1997)
Forsyth, D., Fleck, M.: Body plans. In: CVPR, Puerto Rico, USA, pp. 678–683 (1997)
Kruppa, H., Schiele, B.: Using local context to improve face detection. In: BMVC, Norwich, England, pp. 3–12 (2003)
Mikolajczyk, C., Schmid, K., Zisserman, A.: Human Detection Based on a Probabilistic Assembly of Robust Part Detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Analysis and Machine Intelligence 23(4), 349–361 (2001)
Prince, S.J.D., Elder, J.H., Hou, Y., Sizintsev, M., Olevskiy, Y.: Statistical cue integration for foveated wide-field surveillance. In: Proc. Conf. Computer Vision and Pattern Recognition, San Diego, USA, pp. II: 603–610 (2005)
Schneiderman, H.: Learning Statistical Structure for Object Detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 434–441. Springer, Heidelberg (2003)
Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. Int. Journal of Computer Vision 56(3), 151–177 (2004)
Sidenbladh, H., Black, M.: Learning image statistics for bayesian tracking. In: ICCV, vancouver, canada, pp. 709–716 (2001)
Torralba, A.: Contextual priming for object detection. Int. Journal of Computer Vision 53(2), 169–191 (2003)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Conf. Computer Vision and Pattern Recognition. IEEE Computer Society Press, Los Alamitos (2001)
Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oskarsson, M., Åström, K. (2006). Maximum Likelihood Estimates for Object Detection Using Multiple Detectors. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_72
Download citation
DOI: https://doi.org/10.1007/11815921_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
Online ISBN: 978-3-540-37241-7
eBook Packages: Computer ScienceComputer Science (R0)