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The State of the Art in Image and Video Retrieval

  • Nicu Sebe
  • Michael S. Lew
  • Xiang Zhou
  • Thomas S. Huang
  • Erwin M. Bakker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2728)

Abstract

Image and video retrieval continues to be one of the most exciting and fastest-growing research areas in the field of multimedia technology. What are the main challenges in image and video retrieval? Despite the sustained efforts in the last years, we think that the paramount challenge remains bridging the semantic gap. By this we mean that low level features are easily measured and computed, but the starting point of the retrieval process is typically the high level query from a human. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. However, the semantic gap is not merely translating high level features to low level features. The essence of a semantic query is understanding the meaning behind the query. This can involve understanding both the intellectual and emotional sides of the human, not merely the distilled logical portion of the query but also the personal preferences and emotional subtons of the query and the preferential form of the results.

Keywords

Lecture Note Image Retrieval Relevance Feedback Video Retrieval Sport Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    M. Addis, M. Boniface, S. Goodall, P. Grimwood, S. Kim, P. Lewis, K. Martinez, and A. Stevenson. Integrated image content and metadata search and retrieval across multiple databases. In International Conference on Image and Video Retrieval, pages 88–97. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  2. [2]
    N. Arica and F. Yarman-Vural. A compact shape descriptor based on the beam angle statistics. In International Conference on Image and Video Retrieval, pages 148–157. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  3. [3]
    M. Baillie and J.M. Jose. Audio-based event detection for sports video. In International Conference on Image and Video Retrieval, pages 288–297. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  4. [4]
    L. Barcelo, X. Oriols, and X. Binefa. Spatio-temporal decomposition of sport events for video indexing. In International Conference on Image and Video Retrieval, pages 418–427. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  5. [5]
    Y. Cao, W. Tavanapong, and K. Kim. Audio-assisted scene segmentation for story browsing. In International Conference on Image and Video Retrieval, pages 428–437. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  6. [6]
    Z. Chen, J. Ding, M. Zhang, and W. Tavanapong. Hierarchical clustering-merging in multidimensional index structures. In International Conference on Image and Video Retrieval, pages 78–87. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  7. [7]
    I. Cohen, N. Sebe, Y. Sun, M.S. Lew, and T.S. Huang. Evaluation of expression recognition techniques. In International Conference on Image and Video Retrieval, pages 178–187. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  8. [8]
    N. Dimitrova. Multimedia content analysis: The next wave. In International Conference on Image and Video Retrieval, pages 8–17. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  9. [9]
    J.P. Eakins, K. Jonathan Riley, and J.D. Edwards. Shape feature matching for trademark image retrieval. In International Conference on Image and Video Retrieval, pages 28–37. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  10. [10]
    P.G.B. Enser and C.J. Sandom. Towards a comprehensive survey of the semantic gap in visual image retrieval. In International Conference on Image and Video Retrieval, pages 279–287. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  11. [11]
    A.A. Goodrum, M.M. Bejune, and A.C. Siochi. A state transition analysis of image search patterns on the web. In International Conference on Image and Video Retrieval, pages 269–278. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  12. [12]
    D. Heesch, A. Yavlinski, and S. Rüger. Performance comparison of different similarity models for CBIR with relevance feedback. In International Conference on Image and Video Retrieval, pages 438–447. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  13. [13]
    C-H. Hoi, W. Wang, and M. Lyu. A novel scheme for video similarity detection. In International Conference on Image and Video Retrieval, pages 358–367. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  14. [14]
    N.R. Howe. A closer look at boosted image retrieval. In International Conference on Image and Video Retrieval, pages 58–67. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  15. [15]
    A. Hughes, T. Wilkens, B.M. Wildemuth, and G. Marchionini. Text or pictures? An eyetracking study of how people view digital video surrogates. In International Conference on Image and Video Retrieval, pages 259–268. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  16. [16]
    A. Jaimes, B.L. Tseng, and J.R. Smith. Modal keywords, ontologies, and reasoning for video understanding. In International Conference on Image and Video Retrieval, pages 239–248. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  17. [17]
    F. Jing, M. Li, L. Zhang, H-J. Zhang, and B. Zhang. Learning in region-based image retrieval. In International Conference on Image and Video Retrieval, pages 198–207. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  18. [18]
    A. Joly, C. Frelicot, and O. Buisson. Robust content-based video copy identification in a large reference database. In International Conference on Image and Video Retrieval, pages 398–407. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  19. [19]
    S. Kim, S. Park, and M. Kim. Central object extraction for object-based image retrieval. In International Conference on Image and Video Retrieval, pages 38–47. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  20. [20]
    J. Lay and L. Guan. Concept-based retrieval of art documents. In International Conference on Image and Video Retrieval, pages 368–377. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  21. [21]
    T. Liu and J.R. Kender. Spatial-temporal semantic grouping of instructional video content. In International Conference on Image and Video Retrieval, pages 348–357. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  22. [22]
    X. Liu, A. Srivastva, and D. Sun. Learning optimal representations for image retrieval applications. In International Conference on Image and Video Retrieval, pages 48–57. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  23. [23]
    Y. Liu and J.R. Kender. Fast video retrieval under sparse training data. In International Conference on Image and Video Retrieval, pages 388–397. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  24. [24]
    K. Miura, R. Hamada, I. Ide, S. Sakai, and H. Tanaka. Associating cooking video segments with preparation steps. In International Conference on Image and Video Retrieval, pages 168–177. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  25. [25]
    H. Miyamori. Automatic annotation of tennis action for content-based retrieval by integrated audio and visual information. In International Conference on Image and Video Retrieval, pages 318–327. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  26. [26]
    P. Mulhem and J-H. Lim. Home photo retrieval: Time matters. In International Conference on Image and Video Retrieval, pages 308–317. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  27. [27]
    M. Naphade and J.R. Smith. A hybrid framework for detecting the semantics of concepts and context. In International Conference on Image and Video Retrieval, pages 188–197. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  28. [28]
    H.J. Nock, G. Iyengar, and C. Neti. Speaker localisation using audio-visual synchrony: An empirical study. In International Conference on Image and Video Retrieval, pages 468–477. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  29. [29]
    J-M. Odobez, D. Gatica-Perez, and M. Guillemot. Spectral structuring of home videos. In International Conference on Image and Video Retrieval, pages 298–307. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  30. [30]
    G. Park, Y. Baek, and H-K. Lee. Majority based ranking approach in web image retrieval. In International Conference on Image and Video Retrieval, pages 108–117. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  31. [31]
    S. Park, J. Park, and J.K. Aggarwal. Video retrieval of human interactions using model-based motion tracking and multi-layer finite state automata. In International Conference on Image and Video Retrieval, pages 378–387. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  32. [32]
    M.H. Pi, C.S. Tong, and A. Basu. Improving fractal codes-based image retrieval using histogram of collage errors. In International Conference on Image and Video Retrieval, pages 118–127. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  33. [33]
    M.J. Pickering, L. Wong, and S.M. Rüger. ANSES: Summarisation of news video. In International Conference on Image and Video Retrieval, pages 408–417. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  34. [34]
    F. Qian, B. Zhang, and F. Lin. Constructive learning algorithm-based RBF network for relevance feedback in image retrieval. In International Conference on Image and Video Retrieval, pages 338–347. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  35. [35]
    M. Rautianen, T. Seppanen, J. Pentilla, and J. Peltola. Detecting semantic concepts from video using temporal gradients and audio classification. In International Conference on Image and Video Retrieval, pages 249–258. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  36. [36]
    K. Jonathan Riley, J.D. Edwards, and J.P. Eakins. Content-based retrieval of historical watermark images: II-electron radiographs. In International Conference on Image and Video Retrieval, pages 128–137. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  37. [37]
    M. Rummukainen, J. Laaksonen, and M. Koskela. An efficiency comparison of two content-based image retrieval systems, GIFT and PicSOM. In International Conference on Image and Video Retrieval, pages 478–487. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  38. [38]
    J.M. Sanchez, X. Binefa, and J.R. Kender. Combining multiple features in temporal models for the representation of visual contents in video. In International Conference on Image and Video Retrieval, pages 208–217. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  39. [39]
    Y. Sawahata and K. Aizawa. Indexing of personal video captured by a wearable imaging system. In International Conference on Image and Video Retrieval, pages 328–337. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  40. [40]
    G.J. Scott and C-R. Shyu. EBS k-d tree: An entropy balanced statistical k-d tree for image databases with ground-truth labels. In International Conference on Image and Video Retrieval, pages 448–457. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  41. [41]
    H. Shao, T. Svoboda, T. Tuytelaars, and L. van Gool. HPAT indexing for fast object/scene recognition based on local appearance. In International Conference on Image and Video Retrieval, pages 68–77. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  42. [42]
    C-B. Shim and J-W. Chang. Efficient similar trajectory-based retrieval for moving objects in video databases. In International Conference on Image and Video Retrieval, pages 158–167. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  43. [43]
    A. Smeaton and P. Over. TRECVID: Benchmarking the effectiveness of information retrieval tasks in video. In International Conference on Image and Video Retrieval, pages 18–27. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  44. [44]
    M. Uysal and F. Yarman-Vural. Selection of the best representative feature and membership assignment for content-based fuzzy image database. In International Conference on Image and Video Retrieval, pages 138–147. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.Google Scholar
  45. [45]
    A. Velivelli, C-W. Ngo, and T.S. Huang. Detection of documentary scene changes by audio-visual fusion. In International Conference on Image and Video Retrieval, pages 218–228. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  46. [46]
    H. Wu, H. Lu, and S. Ma. Multilevel relevance judgment, loss function, and performance measure in image retrieval. In International Conference on Image and Video Retrieval, pages 98–107. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  47. [47]
    R. Yan, A. Hauptmann, and R. Jin. Multimedia search with pseudo-relevance feedback. In International Conference on Image and Video Retrieval, pages 229–238. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar
  48. [48]
    H. Ye and G. Xu. Fast search in large-scale image database using vector quantization. In International Conference on Image and Video Retrieval, pages 458–467. Lecture Notes in Computer Science, vol. 2728, Springer, 2003.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nicu Sebe
    • 1
  • Michael S. Lew
    • 2
  • Xiang Zhou
    • 3
  • Thomas S. Huang
    • 4
  • Erwin M. Bakker
    • 2
  1. 1.University of AmsterdamThe Netherlands
  2. 2.Leiden UniversityThe Netherlands
  3. 3.Siemens Corporate ResearchUSA
  4. 4.University of Illinois at Urbana-ChampaignUSA

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