Abstract
This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only, and the weak classifiers use a simple decision stump. The second uses PSO for both selecting good features and evolving weak classifiers in parallel. These two methods are examined and compared on two challenging object detection tasks in images: detection of individual pasta pieces and detection of a face. The experimental results suggest that both approaches can successfully detect object positions and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only. We also show that PSO can evolve and select meaningful features in the face detection task.
Similar content being viewed by others
References
Abramson Y, Moutarde F, Steux B, Stanciulescu B (2006) Combining adaboost with a hill-climbing evolutionary feature-search for efficient training of performant visual object detectors. In: 7th International FLINS conference on applied artificial intelligence (FLINS’06), Genova, Italy, pp 737–744
Bargeron D, Viola P, Simard P (2005) Boosting-based transductive learning for text detection. In: Eighth international conference on document analysis and recognition, vol 2, pp 1166–1171
Bartlett MS, Littlewort G, Fasel I, Movellan JR (2003) Real time face detection and facial expression recognition: development and application to human computer interaction. In: CVPR workshop on computer vision and pattern recognition for human–computer interaction, pp 139–157
Bradski G, Kaehler A, Pisarevsky V (2005) Learning-based computer vision with Intel’s open source computer vision library. Intel Technol J 9(2):119–130
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: A survey and categorisation. J Inf Fusion (Special issue on Diversity in Multiple Classifer Systems) 6:5–20
Cagnoni S, Mordonini M, Sartori J (2007) Particle swarm optimization for object detection and segmentation. In Giacobini M, Brabazon A, Cagnoni S, Caro GD, Drechsler R, Farooq M, Fink A, Lutton E, Machado P, Minner S, O’Neill M, Romero J, Rothlauf F, Squillero G, Takagi H, Uyar S, Yang S (eds) EvoWorkshops. Lecture Notes in Computer Science, vol 4448, Springer, Berlin, pp 241–250
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of international conference on machine learning, pp 148–156
Garcia C, Delakis M (2004) Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans Pattern Anal Mach Intell 26(11):1408–1423
Goldberg D (1989) Genetic algorithms in search, optimisation and machine learning. Addison Wesley, Reading, MA
Hidaka A, Kurita T (2008) Fast training algorithm by particle swarm optimization and random candidate selection for rectangular feature based boosted detector. In: Proceedings of 2008 IEEE international joint conference on neural networks, pp 1163–1169
Huang LL, Shimizu A, Kobatake H (2005) Robust face detection using gabor filter features. Pattern Recognit Lett 26:1614–1649
Ji C, Ma S (1997) Combinations of weak classifiers. IEEE Trans Neural Netw 8:32–42
Jian W, Xue YC, Qian JX (2004) An improved particle swarm optimization algorithm with neighborhoods topologies. In: Proceedings of 2004 international conference on machine learning and cybernetics, vol 4, pp 2332–2337
Kearns MJ, Valiant LG (1994) Cryptographic limitations on learning boolean formulae and finite automata. J ACM 1:67–95
Kearns MJ, Valiant LG (2003) The boosting approach to machine learning: an overview. In: Nonlinear estimation and classification. Springer, Heidelberg
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, NJ, pp 1942–1948
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231–238
Li S, Zhang Z (2004) Floatboost learning and statistical face detection. IEEE Trans Pattern Anal Mach Intell 26(9):1112–1123
Li X, Wang L, Sung E (2005) A study of adaboost with svm based weak learners. In: Proceedings of 2005 IEEE international joint conference on neural networks, vol 1, pp 196–201
Maurice C (1999) The swarm and queen: towards a deterministic and adaptive particle swarm optimization. In: IEEE congress on evolutionary computation, vol 2, pp 1951–1957
Metz CE (1986) ROC methodology in radiologic imaging. Invest Radiol 21(9):720–732
McCane B, Novins K (2003) On training cascade face detectors. In: Image and vision computing, pp 239–244
Omran MG, Engelbrecht AP, Salman AA (2006) Particle swarm optimization for pattern recognition and image processing. In Abraham A, Grosan C, Ramos V (eds) Swarm intelligence in data mining. Studies in computational intelligence, vol 34, Springer, pp 125–151
Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198
Poli R (2006) GECCO 2006 pasta segmentation competition. http://cswww.essex.ac.uk/staff/rpoli/GECCO2006/
Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl 2008:1–10
Rasolzadeh B, Petersson L, Pettersson N (2006) Response binning: improved weak classifiers for boosting. In: IEEE intelligent vehicles symposium (IV2006), pp 344–349
Rowley H, Baluja S, Kanade T (1996) Neural network-based face detection. In: Proceedings of 1996 IEEE computer society conference on computer vision and pattern recognition (CVPR’96), pp 203–208
Sierra A, Echeverria A (2006) Evolutionary discriminant analysis. IEEE Trans Evol Comput 10(1):81–92
Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51
Tanwani AK, Afridi J, Shafiq MZ, Farooq M (2009) Guidelines to select machine learning scheme for classification of biomedical datasets. In: Pizzuti C, Ritchie MD, Giacobini M (eds) EvoBIO. Lecture Notes in Computer Science, vol 5483, Springer, pp 128–139
Treptow A, Zell A (2004) Combining adaboost learning and evolutionary search to select features for real-time object detection. In: Congress on evolutionary computation, vol 2, pp 19–23
Valentini G, Masulli F (2002) Ensembles of learning machines. In: Proceedings of the 13th Italian workshop on neural nets (Lecture Notes in Computer Science) vol 2468. pp 3–19
Verschae R, del Solar JR, Correa M (2006) Gender classification of faces using adaboost. Lecture Notes in Computer Science, vol 4225. pp 68–78
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), vol 1, pp 511–518
Viola P, Jones M, Snow D (2003) Detecting pedestrians using pattern of motion and appearance. In: ICCV, pp 734–741
Yang MH, Kriegman D, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24(1):34–58
Acknowledgments
We would like to thank the anonymous referees for their time, comments, and suggestions that provided great help for improving the paper. This work was supported in part by the University Research Fund under the number of URF09-2399/85808 at Victoria University of Wellington for 2008/2009, and the Marsden Fund council from the government funding (08-VUW-014), administrated by the Royal Society of New Zealand.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mohemmed, A., Johnston, M. & Zhang, M. Particle swarm optimisation based AdaBoost for object detection. Soft Comput 15, 1793–1805 (2011). https://doi.org/10.1007/s00500-010-0615-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-010-0615-x