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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Object-based Image Retrieval Using Dominant Color Pair and Color Correlogram

Dominant 컬러쌍 정보와 Color Correlogram을 이용한 객체기반 영상검색

  • Published : 2003.03.01
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Abstract

This paper proposes an object-based image retrieval technique based on the dominant color pair information. Most of existing methods for content based retrieval extract the features from an image as a whole, instead of an object of interest. As a result, the retrieval performance tends to degrade due to the background colors. This paper proposes an object based retrieval scheme, in which an object of interest is used as a query and the similarity is measured on candidate regions of DB images where the object may exist. From the segmented image, the dominant color pair information between adjacent regions is used for selecting candidate regions. The similarity between the query image and DB image is measured by using the color correlogram technique. The dominant color pair information is robust against translation, rotation, and scaling. Experimental results show that the performance of the proposed method has been improved by reducing the errors caused by background colors.

본 논문에서는 컬러 영상에서 Dominant 컬러쌍 정보를 이용한 객체기반 영상검색 기법을 제안한다. 기존의 대부분 연구에서는 관심있는 객체를 포함한 영상 전체에 대해 특징값을 추출하여 유사 영상을 검색함으로써 배경으로 인해 검색 성능이 나빠지는 단점이 있었다. 본 논문에서는 관심있는 객체 정보만 질의로 사용하고 DB내의 영상들에 대해서도 객체가 존재할 수 있는 후보 영역을 추출한 후 유사도를 측정하는 방법을 제안한다. 제안하는 기법은 평탄 컬러 영역들이 이웃하고 있는 경계부분에서의 Dominant 컬러쌍 정보를 추출하여 특징값으로 사용하였으며, 유사도는 색상을 이용한 Color Correlogram 방법을 사용하였다. 제안하는 Dominant 컬러쌍 특징값은 이동, 회전, 그리고 크기변화에 강건한 특성을 갖는다. 질의 객체 영상에 대해서 DB내에 있는 각각의 영상에 대해 영상 전체를 비교하는 것이 아니라 객체가 존재하는 영역을 추출한 후 유사도를 측정함으로써, 배경 컬러에 의해 영상이 잘못 검출되는 오류가 줄고, 검색 성능이 향상됨을 실험을 통해 확인하였다.

Keywords

References

  1. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkobic and W. Equitz, 'Efficient and Effective Querying by Image Content', Journal of Intelligent Information Systems, Vol. 3, pp. 231-262, 1994 https://doi.org/10.1007/BF00962238
  2. R. Brunelli and O. Mich, 'Image Retrieval by Examples', IEEE Trans. on Multimedia, Vol. 2, No. 3, pp. 164-171, Sep 2000 https://doi.org/10.1109/6046.865481
  3. A. Yoshitaka and T. Ichikawa, 'A Survey on Content-based Retrieval for Multimedia Data-bases', IEEE Trans. on Knowledge and Data Engineering, Vol. 11, No. 1, pp. 81-93, 1999 https://doi.org/10.1109/69.755617
  4. J. K. Wu, 'Content-based Indexing of Multimedia Databases', IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 6, pp. 978-989, 1997 https://doi.org/10.1109/69.649320
  5. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkobic and W. Equitz, 'Efficient and Effective Querying by Image Content', Journal of Intelligent Information Systems, Vol. 3, pp. 231-262, 1994 https://doi.org/10.1007/BF00962238
  6. W. Y. Ma and B. S. Manjunath, 'Netra: A Toolbox for Navigating Large Image Database', IEEE International Conference on Image Processing, pp. 568-571, 1997 https://doi.org/10.1109/ICIP.1997.647976
  7. J. R. Smith and S. F. Chang, 'VisualSEEk : A Fully Automated Content-based Image Query System', ACM Multimedia, Boston MA, 1996 https://doi.org/10.1145/244130.244151
  8. M. Das, E. M. Riseman and B. Draper, 'Focus: Searching for Multi-colored Objects in a Diverse Image Database,' Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997 https://doi.org/10.1109/CVPR.1997.609411
  9. D. Wang, 'Unsupervised Video Segmentation-based on Watersheds and Temporal Tracking', IEEE Trans. on Circuits and System for Video Technology, Vol. 8, No. 5, pp. 539-546, 1998 https://doi.org/10.1109/76.718501
  10. B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, Akio Yamada 'Color and Texture Descriptors', IEEE Tans. Circuits and Systems for Video Technology. Vol. 11, No. 6, June 2001
  11. J. Matas, R. Marik, J. Kittler, 'On representation and matching of multi-coloured objects', Proc. of IEEE International Conference on Computer Vision, pp. 726-732, June. 1995 https://doi.org/10.1109/ICCV.1995.466866
  12. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih, 'Image Indexing Using Color Correlograms', Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762-768, 1997 https://doi.org/10.1109/CVPR.1997.609412
  13. M. J. Swain, 'Color Indexing', International Journal of Computer Vision. Vol. II -32, pp. 11-32, 1991 https://doi.org/10.1007/BF00130487
  14. G. Pass and R. Zabih, 'Histogram Refinement for Content-based Image Retrieval', ACM, Journal of Multimedia System, Vol 7. No. 3 pp. 234-240, 1999 https://doi.org/10.1007/s005300050125
  15. ISO/IEC JTC1/SC29/WG1 'Core Experiment on MPEG-7 Color and Texture Descriptors', Doc. N2819, MPEG Vancouber Meeting, July 1999