Channel Color Energy Feature Representing Color and Texture in Content-Based Image Retrieval

내용기반 영상검색에서 색과 질감을 나타내는 채널색에너지

  • Jung Jae Woong (WiderThan.Com Co. Ltd.) ;
  • Kwon Tae Wan (Department of Electronics Engineering, Graduate School, Hallym University) ;
  • Park Seop Hyeong (Division of Information and Telecommunications Engineering, Graduate School, Hallym University)
  • 정재웅 ((주)와이더덴닷컴) ;
  • 권태완 (한림대학교 대학원 전자공학과) ;
  • 박섭형 (한림대학교 정보통신공학부)
  • Published : 2004.01.01

Abstract

In the field of content-based image retrieval, many numerical features have been proposed for representing visual image content such as color, torture, and shape. Because the features are assumed to be independent, each of them is extracted without ny consideration of the others. In this paper, we consider the relationship between color and texture and propose a new feature called CCE(channel color energy). Simulation results with natural images show that the proposed method outperforms the conventional regular weighted comparison method and SCFT(sequential chromatic Fourier transform)-based color torture method.

내용기반 영상검색 분야에서 색, 질감, 모양 등과 같은 영상의 시각적인 내용을 표현하기 위하여 수치화한 특징들이 많이 제안되었다. 이런 특징들은 모두 독립적이라고 가정하기 때문에 한 특징 벡터를 추출할 때는 다른 특징들과의 상관성을 전혀 고려하지 않는다. 이 논문에서는 색과 질감 사이의 관계를 고려하여 새로운 CCE(channel color energy) 특징을 제안한다. 자연 영상을 대상으로 한 실험결과를 분석한 결과 제안하는 방법이 정규 가중거리 비교 방법과 SCFT(sequential chromatic Fourier transform) 기반 색 질감 방법에 비해 우수한 성능을 보이는 것을 확인할 수 있었다.

Keywords

References

  1. Young Rui and Thoas S. Hang, Shih-Fu Chang 'Image Retrieval: Current techniques, promising directions, and open issues,' Journal of Visual Communication and Image Representation, vol. 10, pp. 39-62, 1999 https://doi.org/10.1006/jvci.1999.0413
  2. P. Piamsa-NGA, N. A. Alexandridis, S. Srakaew, G. Blankenship, G. Papakonstantinou, P. Tsanakas and S. Tzafestas, 'Multi-Feature Content Based Image Retrieval,' Proc. of International Conference on Computer Graphics and Imaging, 1998
  3. Jing Huang, Color-Spatial Image Indexing and Applications, Phd Thesis, Cornell University, August 1998
  4. M. J. Swain and D. H. Ballard,' Color Indexing,' International Journal of Computer Vision, vol. 7, no. 1, pp.11-32, 1991 https://doi.org/10.1007/BF00130487
  5. Jing Huang, S. Ravi Kumar, Mandar Mitra, Wei-Jing Zhu and Ramin Zabih, 'Image Indexing Using Color Correlograms,' Proc. of IEEE Computer Vision and Pattern Recognition Conference. San Juan, Puerto Rico, June 1997 https://doi.org/10.1109/CVPR.1997.609412
  6. R. Haralick, 'Statistical and Structural Approaches to Texture,' Proc. of the IEEE, vol. 67, no. 5, pp.786-804, 1979 https://doi.org/10.1109/PROC.1979.11328
  7. H. Tamura, S. Mori, and T. Yamawaki, 'Texture features corresponding to visual perception,' IEEE Trans. Sys. Man, and Cybernetics, vol. 8, no. 6, pp.460-473, 1978 https://doi.org/10.1109/TSMC.1978.4309999
  8. B. S. Manjunath and W. Y. Ma, 'Texture Features for Browsing and Retrieval of Image Data,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.10, no, Aug, 1996 https://doi.org/10.1109/34.531803
  9. Yossi Rubner, Perceptual Metrics for Image Database Navigation, Phd Thesis, Stanford University, May 1999
  10. Christoph Palm and Thomas M. Lehmann, 'Classification of color textures by Gabor filtering,' Machine Graphics & Vision, vol. 22, no. 2/3, pp.195-219, 2002
  11. H.Mller, Wo. Mller, D. McG. Squire, S. M. Maillet and T. Pun, 'Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals, Pattern Recognition Letters, 22, 5, pp. 593-601, 2001 https://doi.org/10.1016/S0167-8655(00)00118-5