The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Wine Label Recognition System using Image Similarity

이미지 유사도를 이용한 와인라벨 인식 시스템

  • 정종문 (전남대학교 전자컴퓨터공학부) ;
  • 양형정 (전남대학교 전자컴퓨터공학부) ;
  • 김수형 (전남대학교 전자컴퓨터공학부) ;
  • 이귀상 (전남대학교 전자컴퓨터공학부) ;
  • 김선희 (전남대학교 전자컴퓨터공학부)
  • Received : 2011.04.08
  • Accepted : 2011.05.09
  • Published : 2011.05.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently the research on the system using images taken from camera phones as input is actively conducted. This paper proposed a system that shows wine pictures which are similar to the input wine label in order. For the calculation of the similarity of images, the representative color of each cell of the image, the recognized text color, background color and distribution of feature points are used as the features. In order to calculate the difference of the colors, RGB is converted into CIE-Lab and the feature points are extracted by using Harris Corner Detection Algorithm. The weights of representative color of each cell of image, text color and background color are applied. The image similarity is calculated by normalizing the difference of color similarity and distribution of feature points. After calculating the similarity between the input image and the images in the database, the images in Database are shown in the descent order of the similarity so that the effort of users to search for similar wine labels again from the searched result is reduced.

최근 휴대폰 카메라로 촬영한 영상을 입력으로 사용하는 시스템에 대한 연구가 활발히 이루어지고 있다. 본 논문에서는 와인라벨의 문자를 인식한 후, 데이터베이스내의 와인이미지들 중에서 입력 와인라벨 이미지와 유사한 순서대로 사용자에게 보여주는 시스템을 제안한다. 이미지의 유사도 계산을 위해 본 논문에서는 이미지의 각 영역별 대표색상, 텍스트 영역의 텍스트 색상과 배경색상, 그리고 특징점의 분포를 특징으로 사용한다. 이미지의 색상차를 계산하기 위해 RGB색상을 CIE-Lab색상으로 변환하여 사용하고, 특징점은 해리스코너 검출 알고리즘을 사용하여 추출한다. 각 셀의 대표 색상차와 텍스트 색상차 및 배경 색상차는 가중치를 적용하여 색상차 유사도를 계산하고 색상차 유사도와 특징점 분포 유사도를 정규화하여 최종 이미지 유사도를 구한다. 본 논문에서는 입력 이미지와 데이터베이스내의 이미지 간의 유사도를 계산하여 유사도 순으로 사용자에게 검색 결과를 보여줌으로써 검색 결과로부터 다시 최대 유사 와인라벨을 수동으로 찾는 노력을 줄일 수 있다.

Keywords

References

  1. E. Bruns and O. Bimber, "Adaptive training of video sets for image recognition on mobile phones," Personal and Ubiquitous Computing, Vol.13, pp.165-178, 2009. https://doi.org/10.1007/s00779-008-0194-3
  2. 염희정, 은성종, 황보택근, "휴대단말기 영상에서의 기하학적 정보를 이용한 차량 번호판 인식," 한국콘텐츠학회 논문지, 제10권, 제10호, pp.1-8, 2010. https://doi.org/10.5392/JKCA.10.10.001
  3. K. Liu, Y. Q. Cheng, and J. Y. Yang, "Algebraic feature extraction for image recognition based on an optimal discriminant criterion," Pattern Recognition, Vol.26, pp.903-911, 1993. https://doi.org/10.1016/0031-3203(93)90056-3
  4. J. Gausemeier, J. Fruend, G, Matysczok, B, Fruederlin, and D, Beier, "Development of a real time image based object recognition method for mobile AR-devices," In Proceedings of Afrigraph, pp.133-139, 2003.
  5. J. R. Smith and S. F. Chang, "VisualSEEk : A Fully Automated Content-Based Image Query System," ACM Multimedia Conference, pp.87-98, 1996.
  6. M. Carson, S. Thomas, J. M. Belongie, and J. Malik, "Blobworld : A system for region-based image indexing and retrieval," International Conference on Visual Information Systems, pp.509-516, 1999.
  7. L. Zhang, F. Lin, and B. Zhang, "A CBIR method based on color-spatial feature," Proceedings of the IEEE Region 10 Conference, Vol.1, pp.166-169, 1999. https://doi.org/10.1109/TENCON.1999.818376
  8. B. G.Prasad, K. K. Biswas, and S. K. Gupta, "Region -based image retrieval using integrated color,shape, and location index," Computer Vision and Image Understanding, Vol.94, pp.193-233, 2004. https://doi.org/10.1016/j.cviu.2003.10.016
  9. A. F. Smeaton, "Content vs. context for multimedia semantics: The case of SenseCam image structuring," 1st Int. Conf. on Semantics and Digital Media Technologies, pp.1-10, 2006.
  10. J. Lim, J. H. Lim, and Q. Tian, "Automatic summarization for personal digital photos," Communications and Signal Processing, and the 4th Pacific Rim Conf. on Multimedia, Vol.3, pp.1536-1540, 2003.
  11. J. H. Lim and Q. Tian, "Home photo content modeling for personalized event-based retrieval," IEEE Multimedia, Vol.10, No.4, pp.28-37, 2003. https://doi.org/10.1109/MMUL.2003.1237548
  12. D. Gavilan, "Mobile Image Categorization and Retrieval using Color Blobs," Tokyo Institute of Technology, 2005.
  13. P. J. Kuo, T. Aoki, and H. Yasuda, "Building personal digital photograph libraries: An approach with ontology-based MPEG7 dozen dimensional digital content architecture," IEEE Proc. Computer Graphics International, pp.482-489, 2004. https://doi.org/10.1109/CGI.2004.1309251
  14. C. C. Venters and M. Cooper, "A Review of Content-based Image Retrieval Systems," Manchester Visualization Centre, JISC Technology Applications, 2000.
  15. J. Eakins and M. Graham, "Content-based Image Retrieval," Tech. Rep. Technical Report TAP-039, JISC Technology Application, 2000.
  16. 이현운, 전준철, "Wavelet 변환과 질감 특성을 이용한 내용기반 영상검색," 한국정보처리학회 춘계 학술발표논문집, 제7권, 제1호, pp.13-14, 2000.
  17. M. Batko, F. Falchi, C. Lucchese, D. Novák, R. Perego, F. Rabitti, J. Sedmidubský, and P. Zezula. "Crawling, Indexing, and Similarity Searching Images on the Web," In Proceedings of the Sixteenth Italian Symposium on Advanced Database Systems, pp.382-389. 2008.
  18. M. Batko, D. Novak, V. Dohnal, and P. Zezula. "Scaling up the Image Content-based Retrieval," In Second DELOS Conference - Working Notes. Pisa, Italy: DELOS Network of Excellence, pp.1-10, 2007.
  19. P. Bolettieri, A. Esuli, F. Falchi, C. Lucchese, R. Perego, T. Piccioli, and F. Rabitti, "CoPhIR: a test collection for content-based image retrieval," CoRR, 2009.
  20. A Esuli, C. N. delle Ricerche-Italy, "MiPai: using the PP-Index to build an efficient and scalable similarity search system," Second International Workshop on Similarity Search and Applications, pp.146-148, 2009.
  21. 강문주, 김덕은, 양동일, 최형진, "관심영역 이미지 검색을 위한 ROI 시스템 구현," 전자상거래학회지, 제6권, 제3호, pp.41-60, 2006.
  22. J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner and W. Niblack, "Efficient Color Histogram Indexing for Quadratic Form Distance Functions," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.17, pp,729-736, 1995. https://doi.org/10.1109/34.391417
  23. J. R. Smith and S. F. Chang, "VisualSeek: a fully automated content-based image query system," Proceedings of ACM Multimedia, pp.87-98, 1996.
  24. T. S. Chua, K. L. Tan and B. C. Qoi, "Fast Signature-based Color-Spatial Image Retrieval," proceedings of the IEEE International Conference on Multimedia Computing and System, pp.362-369, 1997. https://doi.org/10.1109/MMCS.1997.609637
  25. Y. Gong, G. Proietti and C. Faloutsos, "Image Indexing and Retrieval Based on Human Perceptual Color Clustering," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.578-583, 1998. https://doi.org/10.1109/CVPR.1998.698664
  26. W. K. Pratt, Digital Image Processing, 4th Ed., Eastman Kodak Company, Rochester, New York, 2007.
  27. M. Vlachos, Z. Vagena, P. S. Yu, and V. Athitsos, "Rotation Invariant Indexing of Shapes and Line Drawings," In Proc. of ACM Conf. on Information and Knowledge Management, Bremen, Germany, pp.131-138, Oct. 2005.
  28. E. Keogh, "Exact Indexing of Dynamic Time Warping," In Proc. the 28th Int'l Conf. on Very Large Data Bases, Hong Kong, pp.406-417, 2002(8).
  29. E. J. Keogh, L. Wei, X. Xi, M. Vlachos, S. H. Lee, and P. Protopapas, "Supporting Exact Indexing of Arbitrarily Rotated Shapes and Periodic Time Series under Euclidean and Warping Distance Measures," The VLDB Journal, Vol.18, No.3, pp.611-630, 2009. https://doi.org/10.1007/s00778-008-0111-4
  30. R.. S. Hunter and R. W. Harold, The Measurement of Appearance, Wiley Interscience, New York, 1987.
  31. http://sourceforge.net/projects/opencvlibrary/
  32. http://opencv.willowgarage.com/wiki/
  33. A. Kaeheler, B. Gary, and K. Adrian, Learning Open CV: Computer Vision with the Open CV Livrary, O'Reilly Media, 2008.
  34. 오영준, 박광현, 변증남, "템플릿 매칭에 기반한 한글 지문자 인식," 제 20회 신호처리 합동학술대회 논문집, 2007.