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Image Retrieval Using Local Compact DCT-Based Representation

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Pattern Recognition (DAGM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

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Abstract

An image retrieval system based on local affine frames is introduced. The system provides highly discriminative retrieval of rigid objects under a very wide range of viewing and illumination conditions, and is robust to occlusion and background clutter. Distinguished regions of data dependent shape are detected, and local affine frames (coordinate systems) are obtained. Photometrically and geometrically normalised image patches are extracted and used for matching.

Local correspondences are formed either by direct comparison of photometrically normalised colour intensities in the normalised patches, or by comparison of DCT (discrete cosine transform) coefficients of the patches. Experimental results are presented on a publicly available database of real outdoor images of buildings. We demonstrate the effect of the number of DCT coefficients that are used for the matching. Using the DCT, excellent results with a retrieval performance of 100% in rank 1 are achieved, and memory usage is reduced by a factor of 4.

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References

  1. Swain, M., Ballard, D.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991)

    Article  Google Scholar 

  2. Finlayson, G.D., Chatterjee, S.S., Funt, B.V.: Color angular indexing. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 16–27. Springer, Heidelberg (1996)

    Google Scholar 

  3. Liu, F., Picard, R.W.: Periodicity, directionality, and randomness: Wold features for image modeling and retrieval. IEEE PAMI 18, 7–733 (1996)

    Google Scholar 

  4. Mokhtarian, F., Abbasi, S., Kittler, J.: Robust and efficient shape indexing through curvature scale space. In: Proceedings of British MachineVision Conference, Edinburgh, UK, pp. 53–56 (1996)

    Google Scholar 

  5. Mindru, F., Moons, T., Gool, L.V.: Recognizing color patterns irrespective of viewpoint and illumination. In: CVPR, pp. 368–373 (1999)

    Google Scholar 

  6. Tuytelaars, T., Gool, L.V.: Content-based image retrieval based on local affinely invariant regions. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 493–500. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  7. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE PAMI 22, 1349–1380 (2000)

    Google Scholar 

  8. Obdržálek, Š., Matas, J.: Object recognition using local affine frames on distinguished regions. In: The British Machine Vision Conference (BMVC 2002) (2002)

    Google Scholar 

  9. Obdržálek, Š., Matas, J.: Local affine frames for image retrieval. In: Lew, M., Sebe, N., Eakins, J.P. (eds.) CIVR 2002. LNCS, vol. 2383, p. 318. Springer, Heidelberg (2002)

    Google Scholar 

  10. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Rosin, P.L., Marshall, D. (eds.) Proceedings of the British MachineVision Conference, London, UK, BMVA, vol. 1, pp. 384–393 (2002)

    Google Scholar 

  11. Healey, G.: Using color for geometry-insensitive segmentation. Journal of the Optical Society of America 6, 86–103 (1989)

    Google Scholar 

  12. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Inc., Englewood Cliffs (1986)

    Google Scholar 

  13. Shao, H., Svoboda, T., Van Gool, L.: ZuBuD—Zurich Buildings Database for Image Based Recognition. Technical Report 260, Computer Vision Laboratory, Swiss Federal Institute of Technology (2003)

    Google Scholar 

  14. Shao, H., Svoboda, T., Tuytelaars, T., Van Gool, L.: Hpat indexing for fast object/scene recognition based on local appearance. In: International Conference on Image and Video Retrieval (2003) (to appear)

    Google Scholar 

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Obdržálek, Š., Matas, J. (2003). Image Retrieval Using Local Compact DCT-Based Representation. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_63

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  • DOI: https://doi.org/10.1007/978-3-540-45243-0_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

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