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계층적 CNN을 이용한 방송 매체 내의 객체 인식 시스템 성능향상 방안

Performance Improvement of Object Recognition System in Broadcast Media Using Hierarchical CNN

  • 권명규 (호서대학교 벤처대학원 융합공학과) ;
  • 양효식 (삼일회계법인)
  • 투고 : 2017.01.31
  • 심사 : 2017.03.20
  • 발행 : 2017.03.28

초록

본 논문은 계층적 Convolutional Nerual Network(CNN)을 이용한 스마트폰용 객체 인식 시스템이다. 전체적인 구성은 스마트폰과 서버를 연결하여 서버에서 컨볼루셔널 뉴럴 네트워크로 객체 인식을 하고 수집된 데이터를 매칭시켜 스마트폰으로 객체의 상세정보를 전달하는 방법이다. 또한 계층적 컨볼루셔널 뉴럴 네트워크와 단편적 컨볼루셔널 뉴럴 네트워크와 비교하였다. 계층적 컨볼루셔널 뉴럴 네트워크는 88%, 단편적 컨볼루셔널 뉴럴 네트워크는 73%의 정확도를 가지며 15%p의 성능 향상을 보였다. 이를 기반으로 스마트폰과 방송매체와 연동한 T-Commerce 시장 확장의 가능성을 보여준다. 아울러 방송영상을 시청하면서 Information Retrieval, AR/VR 서비스도 제공 가능하다.

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.

키워드

참고문헌

  1. DIGIECO, Trend Spectrum, "India is the only hope for global smartphone market", http://www.digieco.co.kr/KTFront/dataroom/dataroom_weekly_view.action?board_seq=10980, KT, June, 6, 2016
  2. Wang, Sun-Chong. "Artificial neural network." Interdisciplinary Computing in Java Programming. Springer US, 2003. 81-100.
  3. Y. LeCun, Y. Bengio, & G. Hinton, "Deep learning." Nature 521.7553, pp. 436-444, 2015. https://doi.org/10.1038/nature14539
  4. DOI : http://image-net.org/LSVRC/2012/.
  5. R. Girshick, J. Donahue, T. Darrell & J. Malik, "Region-based convolutional networks for accurate object detection and segmentation." IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No. 1 pp. 142-158, 2016. https://doi.org/10.1109/TPAMI.2015.2437384
  6. J. Justin, A. Karpathy, and L. Fei-Fei. "Densecap: Fully convolutional localization networks for dense captioning." arXiv preprint arXiv:1511.07571. 2015.
  7. A. Krizhevsky, I. Sutskever, and G. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. pp. 1097-1105, 2012.
  8. A.. Karpathy, G. Toderici, S. Shetty, T. Leung, R., Sukthankar, & L. Fei-Fei, "Large-scale video classification with convolutional neural networks." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 1725-1732, 2014.
  9. D. Cireşan, U. Meier, J. Masci, L. Gambardella, & J. Schmidhuber, "High-performance neural networks for visual object classification." arXiv preprint arXiv:1102.0183, 2011.
  10. Chan-hee Jeong, ""Head Pose Estimation and Facial Feature Point Alignment based on Deep Learning", Master Thesis, Sejong University, 2016.
  11. Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, & L. Jackel, "Backpropagation applied to handwritten zip code recognition." Neural computation, Vol. 1, No. 4, pp. 541-551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
  12. Y. LeCun, L. Bottou, Y. Bengio, & P. Haffner, "Gradient-based learning applied to document recognition." Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324., 1998 https://doi.org/10.1109/5.726791
  13. J. Matthews, "An introduction to edge detection: The sobel edge detector; 2002." Dostupny na URL: http://www.generation5.org/content/2002/im01.asp (kveten 2007), 2014.
  14. A. Giusti, D. Cireşan, J. Masci, L. Gambardella, & J. Schmidhuber, "Fast image scanning with deep max-pooling convolutional neural networks." arXiv preprint arXiv:1302.1700 , 2013.
  15. L. Bottou, "Large-scale machine learning with stochastic gradient descent." Proceedings of COMPSTAT'2010. Physica-Verlag HD, pp. 177-186, 2010.
  16. N. Srivastava, G. Hinton, A. Krizhevsky, , I. Sutskever, & R. Salakhutdinov, , "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research, Vol. 15, No. 1, pp. 1929-1958, 2014.
  17. J. Deng, W. Dong, R. Socher, L. Li, K. Li, & L. Fei-Fei, Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, CVPR 2009. IEEE Conference on., pp. 248-255, June, 2009.