The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Similarity-Based Subsequence Search in Image Sequence Databases

이미지 시퀀스 데이터베이스에서의 유사성 기반 서브시퀀스 검색

  • Published : 2003.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

본 논문은 다차원 타임 워핑 거리 함수를 이용하여 유사한 이미지 서브시퀀스를 신속하게 검색할 수 있는 색인 방법을 제안한다. 타임 워핑 거리는 시퀀스들의 길이가 다르거나 샘플링 비율이 다른 많은 응용에서 Lp 거리보다 더욱 적합하다. 우리가 제안한 색인 방법은 디스크 기반의 접미어 트리를 색인 구조체로 채택하고, 유사하지 않은 서브시퀀스를 잘못된 누락 없이 잘 여과하기 위해 하한 거리 함수를 사용한다. 이 방법은 특정 차원의 상대적 가중치를 손쉽게 부여하기 위해 정규화를 적용하고 색인 트리를 압축하기 위해 이산화 과정을 수행한다. 메디컬 이미지와 합성 이미지 시퀀스를 대상으로 한 실험은 본 논문에서 제안한 방법이 naive한 방법보다 우수한 성능을 보이고 대용량의 이미지 시퀸스 데이터베이스로의 확장이 용이함을 입증한다.

Keywords

References

  1. D. A. Adjeroh, M. C. Lee and I. King, A distance measure for video sequence similarity matching, In Proc. Int'l Work hop on Multi-Media Database Management Systems, 1998 https://doi.org/10.1109/MMDBMS.1998.709503
  2. R. Agrawal, C. Faloutsos and A. Swami, Efficient Similarity search in sequence databases, In Proc. Int'l Conf. on Foundations of Data Organization and Algorithms(FODO), pp.69-84, 1993 https://doi.org/10.1007/3-540-57301-1_5
  3. R. Agrawal, K. Lin, H. S. Sawhney and K. Shim, Fast similarity search in the presence of noise, scaling, and translation in time-series databases, In Proc. Int'l Conf. on Very Large Data Bases(VLDB), pp.490-501, 1995
  4. R. Agrawal, G. Psaila, E. L. Wimmers and M. Zait, Querying shapes of histories, In Proc. Int'l Conf. on Very Large Data Bases(VLDB), pp.502-514, 1995
  5. E. Ardizzone, M. L. Cascia, A. Avanzato, and A. Bruna, Video indexing using MPEG motion compensation vectors, In Proc. IEEE Int'l Conf. on Multimedia Computing System, pp.490-501, 1999 https://doi.org/10.1109/MMCS.1999.778574
  6. D. J. Berndt and J. Cliford, Finding patterns in time series : A dynamic programming approach, In Advances in Knowledge Discovery and Data Mining, AAAI/MIT, pp.229-248, 1996
  7. P. Bieganski, J. Riedl and J. V. Carlis, Generalized suffix trees for biological sequence data : Applications and implementation,' In Proc. Hawaii Int'l Conf. on System Sciences, 1994 https://doi.org/10.1109/HICSS.1994.323593
  8. T. Bozkaya, N. Yazdani and M. Ozsoyoglu, Matching and indexing sequences of different lengths, In Proc. ACM Int'l Conf. on Information and Knowledge Management(CIKM), pp.128-135, 1997 https://doi.org/10.1145/266714.266880
  9. T. Brinkho, H. P. Kriegel, R. Schneider and B. Seeger, Multi-step Processing of spatial joins, In Proc. ACM Int'l Conf. on Management of Data(SIGMOD), pp.237-246, 1994 https://doi.org/10.1145/191839.191880
  10. M. S. Brown; J. G. Goldin, M. F. McNitt-Gray, L. E. Greaser, A. Sapra, K. T. Li, J. W. Sayre, M. Martin and D. R. Aberle, Knowledge-based segmentation of thoracic CT images for assessment of split lung function, In Proc. Medical Physics, 2000 https://doi.org/10.1118/1.598898
  11. M. S. Brown, M. F. McNitt-Gray, J. G. Goldin, L. E. Greaser, U. M. Hayward, J. W. Sayre, M. K. Arid and D. R. Aberle, Automated measurement of single and total lung volume from CT, Computer Assisted Tomography, pp.632-640, 1999
  12. M. S. Brown, L. S. Wilson, B. D. Doust, R. W. Gill and C. Sun, Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images, Computerized Medical Imaging and Graphics, pp.463-477, 1999 https://doi.org/10.1016/S0895-6111(98)00051-2
  13. M. S. Chen, J. Han and P. S. Yu, Data mining : An overview from database perspective, IEEE Transactions on Knowledge and Data Engineering(TKDE), pp.866-883, 1996 https://doi.org/10.1109/69.553155
  14. K. W. Chu and M. H. Wong, Fast time-series searching with scaling and shifting, In Proc. ACM Symposium on Principles of Database Systems(PODS), pp.237-248, 1999 https://doi.org/10.1145/303976.304000
  15. W. W. Chu, A. F. Cardenas and R. K. Taira, KMeD : a knowledge-based multimedia medical distributed database system, Information Systems, pp.75-96, 1995 https://doi.org/10.1016/0306-4379(95)98556-S
  16. W. W. Chu and K. Chiang, Abstraction of high level concepts from numerical values in databases, In Proc. AAAI Workshop on Knowledge Discovery in Databases, pp.133-144, 1994
  17. G. Das, D. Gunopulos and H. Mannila, Finding similar time series, In Proc. Principles and Practice of Knowledge Discovery in Databases(PKDD), pp.88-100, 1997 https://doi.org/10.1007/3-540-63223-9_109
  18. C. Faloutsos, M. Ranganathan and Y. Manolopoulos, Fast subsequence matching in time-series databases, In Proc. ACM Int'l Conf. on Management of Data(SIGMOD), pp.419-429, 1994 https://doi.org/10.1145/191839.191925
  19. K. Fukunaga and P. M. Narendra, A branch and bound algorithms for computing k-nearest neighbors, IEEE Transactions on Computers, Vol.C-24, No.7, pp.750-753, 1975 https://doi.org/10.1109/T-C.1975.224297
  20. D. Q. Goldin and P. C. Kanellakis, On similarity queries for time-series data : Constraint specification and implementation, In Proc. Constraint Programming, pp.137-153, 1995 https://doi.org/10.1007/3-540-60299-2_9
  21. A. Hampapur and R. Bolle, Feature based indexing for media tracking, In Proc. IEEE Int'l Conf. on Multimedia and Expo(ICME), 2000 https://doi.org/10.1109/ICME.2000.871101
  22. E. J. Keogh and M. J. Pazzani, Scaling up dynamic time warping to massive datasets, In Proc. Principles and Practice of Knowledge Discovery in Databases(PKDD), 1999
  23. S. W. Kim, S. Park and W. W. Chu, An index-based approach for similarity search supporting time warping in large sequence databases, In Proc. IEEE Int'l Conf. on Data Engineering(ICDE), pp.607-614, 2001 https://doi.org/10.1109/ICDE.2001.914875
  24. R. Lienhart, C. Kuhmunch and W. Effelsberg, On the detection and recognition of television commercials, In Proc. IEEE Int'l Conf. on Multimedia Computing and Systems, 1997 https://doi.org/10.1109/MMCS.1997.609763
  25. J. Meng andS.-F. Chang, CVEPS-A compressed video editing and parsing system, In Proc. ACM Multimedia, 1996 https://doi.org/10.1145/244130.244145
  26. R. Mphan, Video sequence matching, In Proc. Int'l Conf. on Acoustics Speech and Signal Processing(ICASSP), 1998
  27. S. Park, W. W. Chu, J. Yoon and C. Hsu, A suffix tree for fast similarity searches of time-warped subsequences in sequence databases, Technical Report UCLA-CS-TR-990005, UCLA, 1999
  28. S. Park, W. W. Chu, J. Yoon and C. Hsu, Efficient searches for similar subsequences of different lengths in sequence databases, In Proc. IEEE Int'l Conf. on Data Engineering(ICDE), pp.23-32, 2000 https://doi.org/10.1109/ICDE.2000.839384
  29. S. Park, S. W. Kim, J. S. Cho and S. Padmanabhan, Prefix-querying : An approach for effective subsequence matching under time warping in sequence databases, In Proc. ACM Int'l Conf. on Information and Knowledge Management(CIKM), pp.255-262, 2001
  30. S. Park, D. Lee and W. W. Chu, Fast retrieval of similar subsequences in long sequence databases, In Proc. IEEE Knowledge and Data Engineering Exchange Workshop(KDEX), pp.60-67, 1999 https://doi.org/10.1109/KDEX.1999.836610
  31. L. Rabinar and B.-H. Juang, Fundamentals of Speech Recognition, Prentice Hall, 1993
  32. D. Ra ei and A. Mendelzon, Similarity-based queries for time series data, Proc. ACM Int'l Conf. on Management of Data(SIGMOD), pp.13-24, 1997 https://doi.org/10.1145/253260.253264
  33. J. M. Sanchez, X. Binefa, J. Vitria and P. Radeva, Local color analysis for scene break detection applied to TV commercials recognition, In Proc. Visual 99, 1999
  34. H. Shatkay and S. B. Zdonik, Approximate queries and resentations for large data sequences,' In Proc. IEEE Int'l Conf. on Data Engineering(ICDE), pp.536-545, 1994
  35. K. Shim, R. Srikant and R. Agrawal, High-dimensional similarity joins, In Proc. IEEE Int'l Conf. on Data Engineering(ICDE), pp.301-311, 1997 https://doi.org/10.1109/ICDE.1997.581814
  36. M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis, and Machine Vision, Chapman Hall, 1993
  37. D. M. Squire, H. Muller and W. Muller, Improving response time by search pruning in content based image retrieval system, using inverted file techniques, In Proc. IEEE Work shop on Content Based Image and Video Libraries, 1990 https://doi.org/10.1109/IVL.1999.781122
  38. G. A. Stephen, String Searching Algorithms, World Scientific Publishing, 1994
  39. A. Vailaya, W. Xiong and A. K. Jain, Query by video clip, In Proc. Int'l Conf. on Pattern Recognition, 1998 https://doi.org/10.1109/ICPR.1998.711299
  40. J. T. Wang, G. Chirn, T. G. Marr, B. Shapiro, D. Shasha and K. Zhang, Combinatorial pattern discovery for scientific data : Some Preliminary results, In Proc. ACM Int'l Conf. on Management of Data(SIGMOD), pp.115-125, 1994 https://doi.org/10.1145/191839.191863
  41. N. Yazdani and M. Ozsoyoglu, Sequence matching of images, In Proc. Int'l Conf. on Statistical and Scientific Database Management(SSDBM), pp.53-62, 1996 https://doi.org/10.1109/SSDM.1996.505915
  42. B. K. Yi, H. V. Jagadish and C. Faloutsos, Efficient retrieval of similar time sequences under time warping, In Proc. IEEE Int'l Conf. on Data Engineering(ICDE), pp.201-208, 1998 https://doi.org/10.1109/ICDE.1998.655778