Image retrieval by multidimensional elastic matching

  • P. Pala
  • S. Santini
Poster Session C: Compression, Hardware & Software, Image Database, Neural Networks, Object Recognition & Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

Effective image retrieval by content from database requires that visual image properties are used instead of textual labels to recover pictorial data. Retrieval by image similarity given a template image is particularly challenging. The difficulty is to derive a similarity measure that combines shape, grey level paterns and texture in a way that closely conforms to human perception. In this paper a system is presented which supports retrieval by image similarity based on elastic template matching. The template can be both a 1D template modeling the contour of an object, and a 2D template modeling a part of an image with a significant grey level pattern. The retrieval process is obtained as a continuous interaction by which the original query of the user can be refined or changed on the basis of the results provided by the system.

Keywords

Image Retrieval Target Image Template Image Query Refinement Optimal Deformation 
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.

References

  1. 1.
    A. K. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8):1233–1244, Aug. 1996.CrossRefGoogle Scholar
  2. 2.
    W. Cody. Querying multimedia data for multiple repositories by content: The GARLIC project. In Proc. on Visual Data Base Systems III, Lausanne, 1995.Google Scholar
  3. 3.
    K.Hirata, T.Kato, “Query by Visual Example: Content-Based Image Retrieval”. In Advances in Database Technology-EDBT'92, A.Pirotte, C.Delobel, G.Gottlob (Eds.), Lecture Notes on Computer Science, Vol.580Google Scholar
  4. 4.
    W.Niblack et alii, “The QBIC Project: Querying Images by Content Using Color, Texture and Shape”. ResReport 9203, IBM Res.Div. Almaden Res.Center, Feb. 1993.Google Scholar
  5. 5.
    S. Geman, D. Geman “Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images”. IEEE Transactions on Pattern Analysis and Machine Vol.6, No.6, November 1984.Google Scholar
  6. 6.
    M.J.Swain, D.H.Gallard, “Color Indexing”. Int.Journal of Computer Vision, Vol.7, No.1, 1991.Google Scholar
  7. 7.
    D.Terzopoulos, D.Metaxas “Dynamic 3D Models with Local and Global Deformations: Deformable Superquadratics”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, No.7, pp. 703–714, 1991.CrossRefGoogle Scholar
  8. 8.
    R.W.Picard, “A Society of Models for Video and Image Libraries”. TechReport 352, MIT Media Lab Perceptual Computing BM Res.Div. Almaden Res.Center, Feb. 1993.Google Scholar
  9. 9.
    A. Del Bimbo, P. Pala “Visual Image Retrieval by Elastic Matching of User Sketches”. IEEE Transactions on PAMI, February 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • P. Pala
    • 1
    • 2
  • S. Santini
    • 1
    • 2
  1. 1.University of FlorenceItaly
  2. 2.University of CaliforniaSan DiegoUSA

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