Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

FRIP System for Region-based Image Retrieval

영역기반 영상 검색을 위한 FRIP 시스템

  • 고병철 (연세대학교 컴퓨터과학과) ;
  • 이해성 (연세대학교 컴퓨터과학과) ;
  • 변해란 (연세대학교 컴퓨터과학과)
  • Published : 2001.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we have designed a region-based image retrieval system, FRIP(Finding Region In the Pictures). This system includes a robust image segmentation scheme using color and texture direction and retrieval scheme based on features of each region. For image segmentation, by using a circular filter, we can protect the boundary of round object and merge stripes or spots of objects into body region. It also combines scaled and shifted color coordinate and texture direction. After image segmentation, in order to improve the storage management effectively and reduce the computation time, we extract compact features from each region and store as index. For user interface, by the user specified constraints such as color-care / don't care. scale-care / dont care, shape-care / dont care and location-care / dont care, the overal/ matching score is estimated and the top Ie nearest images are reported in the ascending order of the final score.

본 논문에서는, 영역 기반 영상 검색 시스템인 FRIP(Finding Region In the Pictures)을 제안한다. 이 시스템은 크게 색상과 방향성 질감 성분을 결합하는 굳건한 영상 분할 알고리즘과, 분할된 각 영역으로부터 특징 정보들을 추출하고 검색하는 3개의 알고리즘을 포함하고 있다. 영역 분할을 위해서, 영상으로부터 확장 및 이동된 색상 좌표계와, 방향성 질감 성분을 추출하여, 본 시스템에서 제안하는 원형필터에 적용시킨다. 원형 필터에 의해, 영역의 경계선이 자연스럽게 유지 될 수 있고, 또한 일반적인 영역 병합 알고리즘에 의해 병합되지 않던 의미 없는 줄무늬나 작은 점 영역들도 몸체 영역으로 병합 될 수 있다. 영상을 분할한 후에, 효율적인 저장 공간의 관리와 특징 정보 계산 시간을 줄이기 위하여 각 영역으로부터 최적의 특징 정보만을 추출하고 이것을 색인화 하여 데이타베이스에 저장하고 검색에 사용한다. 사용자 인터페이스를 위해서는, 영역의 '색상', '크기', '모양', '위치'와 같은 4개의 질의 조건을 주고, 사용자의 요구에 따라 정합 점수를 계산한 뒤, 그 점수에 따라 상위 검색 결과를 보여 주도록 설계되었다.

Keywords

References

  1. Moghaddamzadeh, and N. Bourbakis, 'A fuzzy region growing approach for segmentation of color images,' Pattern Recognition. Vol. 30, No. 6, pp. 867-881, 1997 https://doi.org/10.1016/S0031-3203(96)00084-2
  2. Anil K. Jain, Fundamentals of digital image processing, Prentice hall international editions, 1989
  3. Carson, M.Thomas, S. Belongie, J.M. Hellerstein, and J. Malik, 'Blobworld: A system for region-based image indexing and retrieval,' In Proceeding of International Confe rence Visual Information System, 1999
  4. Christopher C. Yang, Jeffrey J. and Rodriguez, 'Efficient Luminance and Saturation Processing Techniques for Color Images,' Journal of Visual Communication and Image Represen tation, Vol. 8, pp. 263-277, 1997 https://doi.org/10.1006/jvci.1997.0342
  5. Charles E. Jacobs, Adam Finkelstein and David Salesin, 'Fast Multiresolution Image Qu erying,' Proceeding of SIGGRAPH 95, NewYork, 1995 https://doi.org/10.1145/218380.218454
  6. Faloutsos, M. Flickner, W. Niblack, D. Petkovic, W. Equitz and R. Barber 'Efficient and Effective Querying by Image Con tent,' Research Report #RJ 9203(81511), IBM Almanden Research Center, San Jose, Aug. 1993
  7. Sideney Burrus, Ramesh A. Gopinath and Haitao Guo, Introduction Wavelets and Wavelet Transforms, A primer, Prentice-Hall, 1998
  8. Idris and S. Panchanathan, 'Review of Image and Video Indexing Techniques,' Journal of Visual Communication and Image Represen tation, Vol. 8, No.2, June, pp.146-166, 1997 https://doi.org/10.1006/jvci.1997.0355
  9. Guojun Lu, and AtuI Sajjanhar, 'Region-based shape representation and similarity measure suitable for content-based image retrieval,' Multimedia Systems, pp. 165-174, 1999 https://doi.org/10.1007/s005300050119
  10. H. Zhang and D. Zhong, 'A scheme for visual feature based on image indexing,' Storage Re trieval Image Video Database III 2420, pp. 36-46. 1995
  11. J. L. Chen and A. Kundu. 'Rotation and gray scale invariant texture identification using wa velet decomposition and hidden Markov model,' IEEE Transactions on Pattern Analysis, March. pp. 208-214, 1994
  12. 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
  13. L. Cinque, S. Levial, K.A. Olsen and A. Pellicano, 'Color-Based Image Retrieval Using Spatial-Chromatic Histograms,' International Conference on Multimedia Computing and Systems, Florence, Italy. June 7-11, 1999 https://doi.org/10.1109/MMCS.1999.778621
  14. N. Ikonomakis, K.N. Plataniotis, M Zervakis and A.N. Venetsanopoulos, 'Region growing and region merging segmentation,' DSP-97, 1997 https://doi.org/10.1109/ICDSP.1997.628077
  15. Yining Deng, B.S. Manjunath and Hyundoo Shin, 'Color Image Segmentation,' Proceeding of IEEE Conference On Computer Vision and Pattern Recognition, 1999
  16. Yi Tao, William I. and Grosky, 'Spatial color Indexing: A Novel Approach for ontent-Based Image Retrieval,' International Conference on Multimedia omputing and Systems, Florence, Italy. June 7-11, 1999
  17. W.Y.Ma and B.S. Manjunath, 'Netra: A toolbox for navigating large image database,' IEEE International Conference on Image Processing, 1997 https://doi.org/10.1109/ICIP.1997.647976
  18. W. Y. Ma and B. S. Manjunath, 'Image Indexing using a texture dictionary,' Digital Image Storage Archiving Systems 2606, pp. 288-298, 1995 https://doi.org/10.1117/12.227251