Abstract
The article presents the problem of parameter value selection of the multiclass “one against all” approach of an AdaBoost algorithm in tasks of object recognition based on two-dimensional graphical images. AdaBoost classifier with Haar features is still used in mobile devices due to the processing speed in contrast to other methods like deep learning or SVM but its main drawback is the need to assembly the results of binary two-class classifiers in recognition problems. In this paper an original method of selecting the parameter values of the assembling algorithm using many similar face recognition tasks is proposed. The parameter optimization is done by checking all possible vectors of parameter values. The recognition results with optimized parameter values is \(10\,\%\) better in 8-class face database famous48 (http://eti.pg.edu.pl/documents/176468/27493127/famous48.zip) tasks than using random heuristic which can be represented by the average of all possible vectors of parameter values.
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
References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 886–893 (2005)
Dembski, J.: Multiscaled hybrid features generation for adaboost object detection. J. Med. Informatics Technol. 24(2015), 75–82 (2015)
Dembski, J., Smiatacz, M.: Modular machine learning system for training object detection algorithms on a supercomputer. In: Advances in System Science, pp. 353–361 (2010)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)
Küblbeck, C., Ernst, A.: Face detection and tracking in video sequences using the modified census transformation. Image Vis. Comput. 24(6), 564–572 (2006)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Schapire, R.E.: Using output codes to boost multiclass learning problems. In: Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), pp. 313–321 (1997)
Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)
Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class adaboost. Stat. Interface 2, 349–366 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Dembski, J. (2017). Multiclass AdaBoost Classifier Parameter Adaptation for Pattern Recognition. In: Choraś, R. (eds) Image Processing and Communications Challenges 8. IP&C 2016. Advances in Intelligent Systems and Computing, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-319-47274-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-47274-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47273-7
Online ISBN: 978-3-319-47274-4
eBook Packages: EngineeringEngineering (R0)