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A cascading scheme for speeding up multiple classifier systems

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Abstract

The accuracy of multi-class classification problems is improving at a good pace. However, improving the accuracy often leads to slowing down the processing speed. Since employing a large number of classifiers or a combination of them is a time-consuming process, the sluggish behavior is more evident in multiple classifier systems. In this paper, a practical cascading scheme is proposed for boosting the speed of a multiple binary classifier system with no noticeable reduction in recognition rate. The proposed cascading scheme sets a sequence of binary classifiers and applies one classifier at a time to the input. Some effective criteria for a practical ordering of classifiers are introduced, and a fusion of them is verified to be the best. A vehicle make and model recognition (VMMR) system with multiple individual classifiers is presented briefly as a use case for a multi-class classification problem. The experiments done on this VMMR system using two completely different datasets confirm the effectiveness of our scheme. One of the configurations of the proposed scheme results in up to 30% speedup in comparison with the baseline VMMR system with analogous recognition rate. Another configuration of the proposed cascading scheme achieves up to 80% speedup with just a minor drop in accuracy.

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References

  1. Tulyakov S, Jaeger S, Govindaraju V, Doermann D (2008) Review of classifier combination methods. Stud Comput Intell 90:361–386

    Google Scholar 

  2. 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, pp 511–518

  3. Chen X, Yuille AL (2005) A time-efficient cascade for real-time object detection: with applications for the visually impaired. In: IEEE computer society conference on computer vision and pattern recognition-workshops, pp 28–28

  4. Ach R, Luth N, Techmer A (2008) Real-time detection of traffic signs on a multi-core processor. In: IEEE intelligent vehicles symposium, pp 307–312

  5. Angelova A, Krizhevsky A, Vanhoucke V, Ogale A, Ferguson D (2015) Real-time pedestrian detection with deep network cascades. In: 26th British machine vision conference, pp 1–12

  6. Cai Z, VasconcelosN (2015) A real-time cascade pedestrian detection based on heterogeneous features. In: International SoC design conference, pp 187–188

  7. Zhuang X, Kang W, Wu Q (2016) Real-time vehicle detection with foreground-based cascade classifier. IET Image Proc 10(4):289–296

    Article  Google Scholar 

  8. Sochman J, Matas J (2005) WaldBoost—learning for time constrained sequential detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 150–156

  9. Bourdev L, Brandt J (2005) Robust object detection via soft cascade. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 236–243

  10. Ouyang W, Wang K, Zhu X, Wang X (2017) Learning chained deep features and classifiers for cascade in object detection. arXiv:1702.07054

  11. Xu Z, Kusner MJ, Weinberger KQ, Chen M, Chapelle O (2014) Classifier cascades and trees for minimizing feature evaluation cost. J Mach Learn Res 15:2113–2144

    MathSciNet  MATH  Google Scholar 

  12. Biglari M, Soleimani A, Hassanpour H (2017) Part-based recognition of vehicle make and model. In: IET image processing. doi:10.1049/iet-ipr.2016.0969

  13. Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv large Margin Classif 10(3):61–74

    Google Scholar 

  14. NTOU-MMR Dataset (2016) http://mmplab.cs.ntou.edu.tw/mmplab/MMR/MMR.html (Accessed 22 Oct 2016)

  15. Yang L, Luo P, Loy CC Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3973–3981

  16. The PASCAL Visual Object Classes (2015) http://pascallin.ecs.soton.ac.uk/challenges/VOC/ (Accessed 10 Mar 2015)

  17. Felzenszwalb PF, Girshick RB, McAllester D (2010) Cascade object detection with deformable part models. In: IEEE computer society conference on computer vision and pattern recognition, pp 2241–2248

  18. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering and IT Department, Shahrood University of Technology, Shahrood, Iran

    Mohsen Biglari, Ali Soleimani & Hamid Hassanpour

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  1. Mohsen Biglari
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  2. Ali Soleimani
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  3. Hamid Hassanpour
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Correspondence to Mohsen Biglari.

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Biglari, M., Soleimani, A. & Hassanpour, H. A cascading scheme for speeding up multiple classifier systems. Pattern Anal Applic 22, 375–387 (2019). https://doi.org/10.1007/s10044-017-0637-4

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  • DOI: https://doi.org/10.1007/s10044-017-0637-4

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