Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

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

Implementation on the Filters Using Color and Intensity for the Content based Image Retrieval

내용기반 영상검색을 위한 색상과 휘도 정보를 이용한 필터 구현

  • Noh, Jin-Soo (Dept. of Electronic Eng., College of Elec-Info Eng., Chosun University) ;
  • Baek, Chang-Hui (Dept. of Electronic Eng., College of Elec-Info Eng., Chosun University) ;
  • Rhee, Kang-Hyeon (Dept. of Electronic Eng., College of Elec-Info Eng., Chosun University)
  • Published : 2007.01.25
Download PDF
⟨ Previous Next ⟩

Abstract

As the availability of an image information has been significantly increasing, necessity of system that can manage an image information is increasing. Accordingly, we proposed the content-based image retrieval(CBIR) method based on an efficient combination of a color feature and an image's shape and position information. As a color feature, a HSI color histogram is chosen which is known to measure spatial of colors well. Shape and position information are obtained using Hu invariant moments in the luminance of HSI model. For efficient similarity computation, the extracted features(Color histogram, Hu invariant moments) are combined and then measured precision. As a experiment result using DB that was supported by http://www.freefoto.com, the proposed image search engine has 93% precision and can apply successfully image retrieval applications.

영상 정보의 이용도가 증가함에 따라 영상을 효율적으로 관리할 수 있는 시스템의 필요성이 증가하고 있다. 이에 따라, 본 논문에서는 색채 특징과 영상의 형태와 위치 정보의 효율적인 결합에 근거한 내용기반 영상 검색 엔진을 제안한다. 색채 특징으로는 색채의 공간적인 상관관계를 잘 나타내는 HSI 색채 히스토그램을 선택하였고, 형태와 위치 특징들은 HSI의 휘도 성분에서 불변 모멘트를 이용하여 추출하였다. 효율적인 유사도 측정을 위해 추출된 특징(색채 히스토그램, Hu 모멘트)을 결합하여 정확도를 측정하였다. http://www.freefoto.com에서 제공하는 DB를 사용하여 실험한 결과, 제안된 검색엔진은 93%의 정확도를 가지며 성공적으로 영상 검색에 사용될 수 있음을 보였다.

Keywords

References

  1. 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
  2. Greg Pass, Ramin Zabih, Justin Miller, 'Comparing Images Using Color Coherence Vectors,' In Proc. of the 4th ACM International Conference on Multimedia, pp. 65-73, November 1996 https://doi.org/10.1145/244130.244148
  3. J. R. Smith, S. F. Chang, 'Integrated Spatial and Feature Image Query,' Multimedia Systems, vol. 7, pp. 129-140, March 1999 https://doi.org/10.1007/s005300050116
  4. L. Cinque, S. Levialdi, K.A. Olsen, A. Pellicano, 'Color-Based Image Retrieval Using Spatial Chromatic Histograms,' In Proc. of the Multimedia Systems, vol. 2, pp. 969-973, June 1999 https://doi.org/10.1109/MMCS.1999.778621
  5. '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
  6. Jing Huang, S. Ravi Kumar, Mandar Mitra, Wei-Jing Zhu, and Ramin Zabih, 'Image indexing using color correlograms,' in Proc. of Recognition, pp. 762-768, Virginia, USA, July 1997 https://doi.org/10.1109/CVPR.1997.609412
  7. Kian-Lee Tan, Beng Chin Ooi, Chia Yeow Yee, 'An Evaluation of Color-Spatial Retrieval Techniques for large Databases,' Multimedia Tools and Applications, vol. 14, pp. 55-78, 2001 https://doi.org/10.1023/A:1011359607594
  8. Y. Rui and T. S. Huang, 'Image retrieval: Current techniques, promising directions, and open issues,' J. Visual Communication and Image Representation, vol. 10, no. 4, pp. 39-62, Oct. 1999 https://doi.org/10.1006/jvci.1999.0413
  9. 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
  10. 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
  11. 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
  12. D. Feng, W. C. Siu, and H. J. Zhang, Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, Springer, pp. 4-24, 2003
  13. B. M. Mehtre, M. Kankanhalli, and W. F. Lee, 'Shape measures for content based image retrieval: A comparison,' Information Processng & Management, vol. 33, no. 3, pp. 319-337, May 1997 https://doi.org/10.1016/S0306-4573(96)00069-6
  14. Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw Hill, 1965
  15. Morton Nadler and Eric P. Smith, Pattern Recognition Engineering, Wiley-Interscience, pp.197-199, 1993
  16. Cho-Huak Teh and Roland T. Chin, 'On Digital Approximation of Moment invariants,' Computer Vision, Graphics, And Image Processing, Vol. 33, pp. 318-326, 1986 https://doi.org/10.1016/0734-189X(86)90180-5
  17. M. K. Hu, 'Pattern recognition by moment invariants,' Proc. IEEE, vol. 49, no. 9, pp. 1428, Sept. 1961
  18. Y. K. Chun, J. K. Sung and N. C. Kim, 'Image Retrieval using Multiresolution Color and Texture Features in Wavelet Transform Domain,' Journal of The Institute of Electronics Engineers of Korea, Vol. 43-SP, NO. 1, January 2006