Mathematical models of socio-economic processes
A.V. Sheshkus, D.P. Matalov, V.V. Arlazarov, D.P. Nikolaev The study of the ensemble of the computer vision algorithms based on machine learning for object detection and recognition
System analysis in medicine and biology
Cognitive technology
Methods of artificial intelligence and intelligent systems
A.V. Sheshkus, D.P. Matalov, V.V. Arlazarov, D.P. Nikolaev The study of the ensemble of the computer vision algorithms based on machine learning for object detection and recognition

Abstract.

In this paper we study ensemble approach of the machine learning algorithms for object detection and classification problem. Detailed descriptions analysis of such machine learning algorithms as Viola-Jones method and image classification method using convolutional neural networks are given. The analysis of the experimental results shows applicability of the provided approach for solving complex image recognition tasks.

Keywords:

deep learning, Viola-Jones approach, machine vision, object detection, object classification, convolutional neural networks, ensemble of the machine learning algorithms

PP. 29-36.

DOI: 10.14357/20790279190103

References

1. Cheng G., Han J. A survey on object detection in optical remote sensing images //ISPRS Journal of Photogrammetry and Remote Sensing. – 2016. – Vol. 117. – P. 11-28..
2. Sirmacek B., Unsalan C. Urban-area and building detection using SIFT keypoints and graph theory //IEEE Transactions on Geoscience and Remote Sensing. – 2009. – Vol. 47. – №. 4. – P. 1156-1167.
3. Lin X. et al. Semi-automatic road tracking by template matching and distance transform //Urban Remote Sensing Event, 2009 Joint. – IEEE, 2009. – P. 1-7.
4. Baltsavias E.P. Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems //ISPRS journal of photogrammetry and remote sensing. – 2004. – Vol. 58. – №. 3-4. – P. 129-151..
5. Blaschke T., Lang S., Hay G. (ed.). Object-based image analysis: spatial concepts for knowledgedriven remote sensing applications. – Springer Science & Business Media, 2008.
6. Felzenszwalb P.F., Girshick R.B., McAllester D. Cascade object detection with deformable part models //Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. – IEEE, 2010. – P. 2241-2248.
7. Sivic J., Zisserman A. Video Google: A text retrieval approach to object matching in videos // Proceedings Ninth IEEE International Conference on Computer Vision. – IEEE, 2003. – P. 1470.
8. Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks //Advances in neural information processing systems. – 2012. – P. 1097-1105.
9. Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition //arXiv preprint arXiv:1409.1556. – 2014.
10. Viola P., Jones M. Robust Real-time Object Detection // International Journal of Computer Vision. 2002.
11. LeCun Y. et al. Object recognition with gradientbased learning //Shape, contour and grouping in computer vision. – Springer, Berlin, Heidelberg, 1999. – P. 319-345.
12. Dollár P. et al. Integral channel features. – 2009.
13. Kuznetsova E., Shvets E., Nikolaev D. Viola-Jones based hybrid framework for real-time object detection in multispectral images //Eighth International Conference on Machine Vision (ICMV 2015). – International Society for Optics and Photonics, 2015. – Vol. 9875. – P. 98750N.
14. Isukapalli R., Elgammal A., Greiner R. Learning to detect objects of many classes using binary classifiers //Computer Vision–ECCV 2006. – Springer, Berlin, Heidelberg, 2006. – P. 352-364.
15. Usilin S. et al. Visual appearance based document image classification //Image Processing (ICIP), 2010 17th IEEE International Conference on. – IEEE, 2010. – P. 2133-2136.
16. Arlazarov V.V., Matalov D.P., Usilin S.A. Lokalizatsiya obraza pechati na dokumente, udostoveryayushchem lichnost’, metodom mashinnogo obucheniya // Trudy ISA RAN. Spetsvypusk. 2018. P. 158-166.
17. Redmon J. et al. You only look once: Unified, real-time object detection //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2016. – P. 779-788.
18. Liu W. et al. Ssd: Single shot multibox detector //European conference on computer vision. – Springer, Cham, 2016. – P. 21-37.
19. Ilin D. et al. Fast integer approximations in convolutional neural networks using layer-bylayer training //Ninth International Conference on Machine Vision (ICMV 2016). – International Society for Optics and Photonics, 2017. – Vol. 10341.
20. Limonova E., Sheshkus A., Nikolaev D. Computational optimization of convolutional neural networks using separated filters architecture // International Journal of Applied Engineering Research. – 2016. – Vol. 11. – №. 11. – P. 7491-7494.
21. Li H. et al. A convolutional neural network cascade for face detection //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – 2015. – P. 5325-5334.
22. Gayer A.V, Sheshkus A.V., Chernyshova Y.S. Augmentatsiya obuchayushchei vyborki «na letu» dlya obucheniya neironnykh setei // Trudy ISA RAN. Spetsvypusk. 2018. С. 150-157.
23. Liu W. et al. A survey of deep neural network architectures and their applications // Neurocomputing. – 2017. – Vol. 234. – P. 11-26.
24. Dataset lokalizatsii i raspoznavaniya tankov [Electronical Resource] URL: ftp://vis.iitp.ru/tank_recognition (accessed February 13, 2019)
 

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