대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

칼라의 공간적 상관관계 및 국부 질감 특성을 이용한 영상검색

Image Retrieval Using Spacial Color Correlation and Local Texture Characteristics

  • 발행 : 2005.09.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 칼라 특징으로 칼라 오토코렐로그램(autocorrelogram)을 선택하고 질감 특징으로 BDIP(block difference inverse probabilities)와 BVLC(block variance of local correlation coefficient)를 선택하여 이들을 효율적으로 추출하고 결합한 다중 특징기반 영상검색 기법을 제안한다. 칼라 오토코렐로그램은 영상의 H(hue), S(saturation) 칼라 성분으로부터 추출 하였고, BDIP와 BVLC는 V(value) 성분으로부터 추출하였다. 이때 각 특징추출 시 계산량을 고려하여 간소화된 오토코렐로그램과 BVLC를 제안하여 사용하였으며, 추출한 특징들을 효율적으로 저장하기 위해 특징벡터성분들의 값을 그 분포에 따라 균등 또는 비균등 양자화 하여 사용하였다. Corel DB및 VisTex DB에 대한 실험 결과, 칼라 오토코렐로그램과 BDIP, BVLC 질감 특징을 결합함으로써 동일한 차원에서 오토코렐로그램만을 사용할 때보다 최대 9.5%, BDIP, BVLC만을 사용할 때보다 최대 4% 검색성능이 향상되었다. 또한 제안한 다중 특징은 웨이브렛 모멘트, CSD, 칼라 히스토그램에 비해 특징벡터의 저장공간을 약 3분의 1 정도 적게 차지하면서 검색성능이 각각 최대 12.6%, 14.6%, 27.9% 우수하게 나타남을 확인할 수 있었다.

This paper presents a content-based image retrieval (CBIR) method using the combination of color and texture features. As a color feature, a color autocorrelogram is chosen which is extracted from the hue and saturation components of a color image. As a texture feature, BDIP(block difference of inverse probabilities) and BVLC(block variation of local correlation coefficients) are chosen which are extracted from the value component. When the features are extracted, the color autocorrelogram and the BVLC are simplified in consideration of their calculation complexity. After the feature extraction, vector components of these features are efficiently quantized in consideration of their storage space. Experiments for Corel and VisTex DBs show that the proposed retrieval method yields 9.5% maximum precision gain over the method using only the color autucorrelogram and 4.0% over the BDIP-BVLC. Also, the proposed method yields 12.6%, 14.6%, and 27.9% maximum precision gains over the methods using wavelet moments, CSD, and color histogram, respectively.

키워드

참고문헌

  1. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, 'Content-based image retrieval at the end of the early years,' IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000 https://doi.org/10.1109/34.895972
  2. A. Yoshitaka and T. Ichikawa, 'A survey on content-based retrieval for multimedia databases,' IEEE Trans. Knowledge and Data Eng., vol. 11, no. 1, pp. 81-93, Jan.-Feb. 1999 https://doi.org/10.1109/69.755617
  3. Y. Rui and T. S. Hang, 'Image retrieval: Crrent techniques, promising directions, and open issues,' J. Visual Communication and Image Representation, vol. 10, pp. 39-62, 1999 https://doi.org/10.1006/jvci.1999.0413
  4. D. Feng, W. C. Siu, and H. J. Zhang, Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, Springer, pp. 4-24, 2003
  5. 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
  6. 'ISO/IEC 15938-3/FDIS Information Technology - Multimedia Content Description Interface - part 3 Visual,' ISO/IEC/JTC1/SC29/WG11, Doc. N4358, Sydney, Australia, July 2001
  7. Jing Huang, S. Ravi Kumar, Mandar Mitra, Wei-Jing Zhu and Ramin Zabih, 'Image Indexing Using Color Correlograms,' in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition Conference. San Juan, Puerto Rico, June 1997 https://doi.org/10.1109/CVPR.1997.609412
  8. R. M. Haralick, K. Shanmugam, and I. Dinstein, 'Texture features for image classification,' IEEE Trans. Syst. Man Cybern., vol. 8, pp. 610-621, Nov. 1973
  9. K. S. Thyagarajan, T. Nguyen, and C. Persons, 'A maximum likelihood approach to texture classification using wavelet transform,' in Proc. of IEEE Conf. on Image Processing, pp. 640-644, Austin, USA, Nov. 1994 https://doi.org/10.1109/ICIP.1994.413649
  10. Y. D. Chun, S. Y. Seo, and N. C. Kim, 'Image retrieval using BDIP and BVLC moments,' IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 9, pp. 951-957, Sep. 2003 https://doi.org/10.1109/TCSVT.2003.816507
  11. B. M. Mehtre, M. Kankanhalli, and W. F. Lee, 'Shape measures for content based image retrieval: A comparison,' Information Processing & Management, vol. 33, no. 3, pp. 319-337, May 1997 https://doi.org/10.1016/S0306-4573(96)00069-6
  12. T. Gevers and A. W. M. Smeulders, 'PicToSeek: Combining color and shape invariant features for image retrieval,' IEEE Trans. Image Processing, vol. 9, pp. 102-119, Jan. 2000 https://doi.org/10.1109/83.817602
  13. Q. Iqbal and J. K. Aggarwal, 'Combining structure, color, and texture for image retrieval: A performance evaluation,' in Proc. of IEEE Conf. on Pattern Recognition, pp. 438-443, Quebec, Canada, Aug. 2002 https://doi.org/10.1109/ICPR.2002.1048333
  14. H. Permuter, J. Francos, and I. H. Jermyn, 'Gaussian mixture models of texture and colour for image database retrieval,' in Proc. of IEEE Conf. on Acoustics, Speech, and Signal processing, pp. 569-572, Hong Kong, China, Apr. 2003 https://doi.org/10.1109/ICASSP.2003.1199538
  15. 성중기, 칼라의 공간적 상관관계 및 국부 질감 특성을 이용한 영상검색, 경북대학교 석사학위논문, 2004년 12월
  16. Jing Huang, S. R. Kumar, M. Mitra, and Wei-Jing Zhu, 'Spatial color indexing and applications,' in Proc. of Int. Conf. on Computer Vision, pp. 602-607, Bombay, India, Jan. 1998 https://doi.org/10.1109/ICCV.1998.710779
  17. T. Ojala, M. Rautiainen, E. Matinmikko, and M. Aittola, 'Semantic image retrieval with HSV correlogram,' in Proc. of 12th Scandinavian Conf. on Image Anal., pp. 621-627, Bergen, Norway, June 2001
  18. S. P. Lloyd, 'Least squares quantization in PCM,' IEEE Trans. Information Theory, vol. 28, no. 2, pp. 129-137, Mar. 1982 https://doi.org/10.1109/TIT.1982.1056489
  19. S. F. Chang, W. C. Horace J. Meng, H. Sundaram, and D. Zhong, 'A fully automated content-based video search engine supporting spatiotemporal queries,' IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 602-615, Sep. 1998 https://doi.org/10.1109/76.718507
  20. P. Ndjiki-Nya, J. Restat, T. Meiers, J.-R. Ohm, A. Seyferth, and R. Sniehotta, 'Subjective evaluation of the MPEG-7 retrieval accuracy measure (ANMRR),' ISO/WG11 MPEG Meeting, Doc. M6029, Geneva, Switzerland, May 2000