Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Low-Informative Region Detection based on Multi-Layer Perceptron for Automatical Insertion of Virtual Advertisement in Sports Image

스포츠 영상 내에서 자동적인 가상 광고 삽입을 위한 다층퍼셉트론 기반의 저정보 영역 검출

  • Jung, Jae-Young (Department of Computer Science, Dongyang University) ;
  • Kim, Jong-Ha (Department of Architecture & Fire Administration, Dongyang University)
  • 정재영 (동양대학교 컴퓨터학과) ;
  • 김종하 (동양대학교 건축소방행정학과)
  • Received : 2016.10.12
  • Accepted : 2017.02.25
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Virtual advertisement is an advertising technique that using computer graphic in a media production such as a sports image for inserting product image, logo, advertising slogan, etc. Recently, the image insertion of virtual advertisement is actively spreading due to the satisfaction of technical element for the image insertion of virtual advertisement in sports advertisement by increasing of the image processing technology and the computing performance. In addition, image processing technology for automatic insertion has become an important research field in the virtual advertisement field. In this paper, we propose the method of extracting less-informative region by using image processing technique and machine learning to insert a virtual advertisement automatically in sports image. The proposed method analyzes the brightness level of image through the histogram and extracts the less-informative region using the machine learning method.

가상광고는 컴퓨터 그래픽을 이용하여 스포츠영상과 같은 미디어제작영상에 제품의 이미지, 로고, 선전문구 등을 삽입하는 광고기법이다. 최근 영상처리 기술과 컴퓨터 성능의 상승으로 인해 스포츠영상에 가상광고를 삽입하기 위한 기술적인 요소가 충족되어 영상 내에 가상광고의 삽입이 활발하게 진행되고 있다. 또한 자동적인 가상광고 삽입을 위한 영상 처리 기술이 가상광고 영역에서 중요한 연구 분야로 자리 잡고 있다. 이에 본 논문에서는 스포츠 영상 내에서 자동적으로 가상광고를 삽입하기 위해 영상처리 기법과 기계학습을 활용하여 저정보 영역을 추출하는 방법을 제안한다. 제안 방법은 영상의 밝기 정도를 히스토그램을 통해 분석하고 기계학습 방법을 활용하여 저정보 영역을 추출한다.

Keywords

References

  1. Jaeyoung Jung, Youngkab Kim, "Less Informative Region Extraction for Automatically Advertisement Insertion in Sports Image" Journal of Digital Contents Society, Vol.16 No.4 Aug. (2015) 615-622. https://doi.org/10.9728/dcs.2015.16.4.615
  2. Tsuji, Yosuke, Gregg Bennett, and James H. Leigh."Investigating factors affecting brand awareness of virtual advertising." Journal of Sport Management, 23.4 (2009): 511-544. https://doi.org/10.1123/jsm.23.4.511
  3. Deutsch, Askan. "Sports broadcasting and virtual advertising: Defining the limits of copyright law and the law of unfair competition." Marq. Sports L. Rev. 11 (2000): 41.
  4. McEvilly, Theresa E. "Virtual Advertising in Sports Venues and the Federal Lanham Act 43 (a): Revolutionary Technology Creates Controversial Advertising Medium." Seton Hall J. Sport L. 8 (1998): 603.
  5. Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision, 60.2 (2004): 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  6. Mohan, V. M., Durga, R. K., Devathi, S., Raju, K. S. "Image Processing Representation Using Binary Image; Grayscale, Color Image, and Histogram." Proceedings of the Second International Conference on Computer and Communication Technologies Springer India (2016): 353-361.
  7. Ramesh, N., J-H. Yoo, and I. K. Sethi. "Thresholding based on histogram approximation." IEE Proceedings-Vision, Image and Signal Processing 142.5 (1995): 271-279. https://doi.org/10.1049/ip-vis:19952007
  8. Gardner, Matt W., and S. R. Dorling. "Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences." Atmospheric environment 32.14 (1998): 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
  9. Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. "Multilayer feedforward networks are universal approximators." Neural networks 2.5 (1989): 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  10. Hecht-Nielsen, Robert. "Theory of the backpropagation neural network." Neural Networks, 1989. IJCNN., International Joint Conference on. IEEE (1989): 593-605.
  11. Ito, Yoshifusa. "Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory." Neural Networks 4.3 (1991): 385-394. https://doi.org/10.1016/0893-6080(91)90075-G
  12. HyunKyung Song, and YoungJik Lee, "An Analysis on the Role of Hidden Layer and Node Reduction Algorithm in Back-Propagation Learing." Journal of the Institute of Electronics and Information, Engineers-B 28.3 (1991): 207-220.