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Visual Alphabets: Video Classification by End Users

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Multimedia Data Mining and Knowledge Discovery

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Abstract

The work presented here introduces a real-time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two-stage procedure. First, small image fragments called patches are classified.

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References

  1. Israël M, Broek EL van den, Putten P van der, Uyl MJ den. Automating the construction of scene classifiers for Content-Based Video Retrieval. In: Khan L, Petrushin VA, eds. Proceedings of the Fifth ACM International Workshop on Multimedia Data Mining (MDM/KDD'04). Seattle, WA, USA; 2004, pp. 38–47.

    Google Scholar 

  2. Putten P van der. Vicar Video Navigator: Content Based Video Search Engines Become a Reality. Broadcast Hardware International, IBC edition; 1999.

    Google Scholar 

  3. Gibson J. The Ecological Approach to Visual Perception. Houghton Mifflin, Boston; 1979.

    Google Scholar 

  4. Niblack W, Barber R, Equitz W, Flickner M, Glasman E, Petkovic D, et al. The QBIC project: Querying images by content using color, texture, and shape. Proceedings of SPIE (Storage and Retrieval for Image and Video Databases) 1993;1908:173–87.

    Google Scholar 

  5. Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, et al. Query by image and video content: The QBIC system. IEEE Computer 1995;28(9):23–32.

    Google Scholar 

  6. Smith JR, Chang SF. Querying by Color Regions Using the VisualSEEk Content-Based Visual Query System. The AAAI Press; 1997, pp. 23–42.

    Google Scholar 

  7. Cox IJ, Miller ML, Minka TP, Papathomas TV. The bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing 2000;9(1):20–37.

    Article  Google Scholar 

  8. Gevers Th, Smeulders AWM. Pic To Seek: Combining color and shape invariant features for image retrieval IEEE Transactions on Image Processing 2000;9(1):102–19.

    Article  Google Scholar 

  9. Wang JZ. Integrated Region-Based Image Retrieval. Boston: Kluwer Academic Publishers; 2001.

    MATH  Google Scholar 

  10. IBM Research. MARVEL: Multimedia Analysis and Retrieval System. Intelligent Information Management Dept., IBM T. J. Watson Research Center; 2005.

    Google Scholar 

  11. van den Broek EL, Kok T, Schouten ThE, Hoenkamp E. Multimedia for Art ReTrieval (M4ART). Proceedings of SPIE (Multimedia Content Analysis, Management, and Retrieval) 2006;60730Z.

    Google Scholar 

  12. Wu S, Rahman MKM, Chow TWS. Content-based image retrieval using growing hierarchical self-organizing quadtree map. Pattern Recognition 2005;38(5):707–22.

    Article  MATH  Google Scholar 

  13. Minka TP, Picard RW. Interactive learning using a “society of models.” MIT Media Laboratory Perceptual Computing Section; 1996.

    Google Scholar 

  14. Picard RW. Light-years from Lena:Video and image libraries of the future. In: Proceedings of the 1995 International Conference on Image Processing; 1995, pp. 310–3.

    Google Scholar 

  15. Picard RW, Minka TP. Vision Texture for Annotation. Multimedia Systems 1995;3(1):3–14.

    Article  Google Scholar 

  16. Belongie S, Carson C, Greenspan H, Malik J. Recognition of Images in Large Databases Using a Learning Framework. University of California at Berkeley; 1997.

    Google Scholar 

  17. Carson C, Belongie S, Greenspan H, Malik J. Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002;24(8):1026–38.

    Article  Google Scholar 

  18. Campbell NW, Mackeown WPJ, Thomas BT, Troscianko T. The automatic classification of outdoor images. In: Proceedings of the International Conference on Engineering Applications of Neural Networks. Systems Engineering Association; 1996, pp. 339–42.

    Google Scholar 

  19. Campbell NW, Mackeown WPJ, Thomas BT, Troscianko T. Interpreting Image Databases by Region Classification. Pattern Recognition 1997;30(4):555–63.

    Article  Google Scholar 

  20. Lipson P, Grimson E, Sinha P. Configuration based scene classification and image indexing. In: Proceedings of 16th IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society; 1997, pp. 1007–13.

    Google Scholar 

  21. Ratan AL, Grimson WEL. Training templates for scene classication using a few examples. In: Proceedings of the IEEE Workshop on Content-Based Analysis of Images and Video Libraries; 1997, pp. 90–7.

    Google Scholar 

  22. Oliva A, Torralba A. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision 2001;42(3):145–75.

    Article  MATH  Google Scholar 

  23. Gorkani MM, Picard RW. Texture Orientation for Sorting Photos at a Glance. In: Proceedings of the International Conference on Pattern Recognition; 1994, pp. 459–64.

    Google Scholar 

  24. Szummer M, Picard RW. Indoor-outdoor image classification. In: IEEE InternationalWorkshop on Content-Based Access of Image and Video Databases (CAIVD). Bombay, India: IEEE Computer Society; 1998, pp. 42–51.

    Chapter  Google Scholar 

  25. Fung CY, Loe KF. Learning primitive and scene semantics of images for classification and retrieval. In: Proceedings of the 7th ACM International Conference on Multimedia '99. Orlando, Florida, USA: ACM; 1999, pp. 9–12.

    Google Scholar 

  26. Fung CY, Loe KF. A new approach for image classification and retrieval. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM; 1999, pp. 301–2.

    Google Scholar 

  27. Broek EL van den, Rikxoort EM van, Schouten ThE. Human-centered object-based image retrieval. Lecture Notes in Computer Science (Advances in Pattern Recognition) 2005;3687:492–501.

    Google Scholar 

  28. Smith JR, Chang SF. Single color extraction and image query. In: Liu B, editor. Proceedings of the 2nd IEEE International Conference on Image Processing. IEEE Signal Processing Society. IEEE Press; 1995, pp. 528–31.

    Google Scholar 

  29. Broek EL van den, Kisters PMF, Vuurpijl LG. Content-based image retrieval benchmarking: Utilizing color categories and color distributions. Journal of Imaging Science and Technology 2005;49(3):293–301.

    Google Scholar 

  30. Broek EL van den. Human-Centered Content-Based Image Retrieval. Ph.D. thesis. Nijmegen Institute for Cognition and Information, Radboud University Nijmegen; 2005. Available from: http://eidetic.ai.ru.nl/egon/PhD-Thesis/.

    Google Scholar 

  31. Prasad B, Gupta S, Biswas K. Color and shape index for region-based image retrieval. In: Arcelli C, Cordella L, di Baja GSanniti, eds. Proceedings of 4th International Workshop on Visual Form. Capri, Italy: Springer Verlag; 2001, pp. 716–25.

    Google Scholar 

  32. Redfield S, Nechyba M, Harris JG, Arroyo AA. Efficient object recognition using color. In: Roberts R, ed. Proceedings of the Florida Conference on Recent Advances in Robotics. Tallahassee, Florida; 2001.

    Google Scholar 

  33. Schettini R, Ciocca G, Zuffi S. Asurvey of methods for colour image indexing and retrieval in image databases. J. Wiley; 2001.

    Google Scholar 

  34. Forsyth DA, Ponse J. Computer Vision: A Modern Approach. Pearson Education, Inc., Upper Saddle River, NJ; 2002.

    Google Scholar 

  35. Berlin B, Kay P. Basic Color Terms: Their Universals and Evolution. Berkeley: University of California Press; 1969.

    Google Scholar 

  36. Broek EL van den, Rikxoort EM van. Parallel-Sequential Texture Analysis. Lecture Notes in Computer Science (Advances in Pattern Recognition) 2005;3687:532–41.

    Google Scholar 

  37. Derefeldt G, Swartling T. Colour concept retrieval by free colour naming: Identification of up to 30 colours without training. Displays 1995;16(2):69–77.

    Article  Google Scholar 

  38. Derefeldt G, Swartling T, Berggrund U, Bodrogi P. Cognitive color. Color Research & Application 2004;29(1):7–19.

    Article  Google Scholar 

  39. Goldstone RL. Effects of categorization on color perception. Psychological Science 1995;5(6):298–304.

    Article  Google Scholar 

  40. Kay P. Color. Journal of Linguistic Anthropology 1999;1:29–32.

    Google Scholar 

  41. Roberson D, Davies I, Davidoff J. Colour Categories Are Not Universal: Replications and New Evidence From a Stone-Age Culture. Lanham, MD: University Press of America Inc.; 2002.

    Google Scholar 

  42. Lin T, Zhang HJ. Automatic Video scene extraction by shot grouping. In: Proceedings of the 15th IEEE International Conference on Pattern Recognition. vol. 4. Barcelona, Spain; 2000, pp. 39–42.

    Google Scholar 

  43. Gevers Th, Smeulders AWM. Color based object recognition. Pattern Recognition 1999;32(3):453–64.

    Article  Google Scholar 

  44. Jain AK, Karu K. Learning texture discrimination masks. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996;18(2):195–205.

    Article  Google Scholar 

  45. Palm C. Color texture classification by integrative Co-occurrence matrices. Pattern Recognition 2004;37(5):965–76.

    Article  Google Scholar 

  46. Rikxoort EM van, Broek EL van den, Schouten ThE. Mimicking human texture classification. Proceedings of SPIE (Human Vision and Electronic Imaging X) 2005;5666:215–26.

    Google Scholar 

  47. Broek EL van den, Rikxoort EM van, Kok T, Schouten ThE. M-HinTS: Mimicking Humans in Texture Sorting. Proceedings of SPIE (Human Vision and Electronic Imaging XI) 2006;60570X.

    Google Scholar 

  48. Rosenfeld A. From image analysis to computer vision: An annotated bibliography, 1955–1979. Computer Vision and Image Understanding 2001;84(2):298–324.

    Article  MATH  Google Scholar 

  49. Jähne B. Practical Handbook on Image Processing for Scientific Applications. CRC Press; 1997.

    Google Scholar 

  50. Brox T, Weickert J, Burgeth B, Mrázek P. Nonlinear structure tensors. Image and Vision Computing 2006;24(1):41–55.

    Article  Google Scholar 

  51. Tax DMJ. One-Class Classification; Concept Learning in the Absence of Counter- Examples. PhD thesis, Delft University of Technology; 2001.

    Google Scholar 

  52. ChandrinosKV, Androutsopoulos I, Paliouras G, Spyropoulos CD. AutomaticWeb Rating: Filtering Obscene Content on the Web. In: Borbinha J, Baker T, eds. Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries; 2000, pp. 403–6.

    Google Scholar 

  53. Israël M. ParaBot: Text and Image Classification for the Internet. Amsterdam, The Netherlands: Sentient Machine Research; 1999.

    Google Scholar 

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Authors
  1. Menno Israël
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  2. Egon L. van den Broek
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  3. Peter van der Putten
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  4. Marten J. den Uyl
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Editor information

Editors and Affiliations

  1. Accenture Technology Labs, Accenture Ltd, 161 N. Clark St., 60601, Chicago, IL, USA

    Valery A. Petrushin MS, PhD

  2. School of Forest Resources and Conservation, University of Florida, EC 31 2601 N. Floyd Rd., 75080-1407, Richardson, TX, USA

    Latifur Khan BS, MS, PhD

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Israël, M., van den Broek, E.L., van der Putten, P., den Uyl, M.J. (2007). Visual Alphabets: Video Classification by End Users. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_10

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  • DOI: https://doi.org/10.1007/978-1-84628-799-2_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-436-6

  • Online ISBN: 978-1-84628-799-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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