Abstract
Accessing to large image databases is a huge challenge because of the large amount of data required by images. Therefore automatic and efficient indexing is needed for fast content based retrieval, it alleviates the drawback of any manual annotating.
We propose a method for pattern matching into large image databases based on the Fractal Transform. A mathematical representation is associated to the images of the database. This representation is a set of function parameters resulting from a dedicated fractal compression scheme, and used as an index by a retrieval algorithm. It works entirely in the Fractal transform parameter space of both image and pattern, to obtain performances compatible with an interactive search.
The research engine uses both textures and edges of the pattern. The pattern can be present in the image with different orientations and/or scales by using a multi-compression Fractal representation of the pattern.
This method allows to retrieve in 3 seconds a 64 × 64 pixels pattern in an 100 images (512 × 512 pixels) database, on a SUN Sparc 20 workstation. It can be combined with other indexing and retrieval techniques.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Y. Fisher. “Fractal Compression: Theory and Application to Digital Images∝, SpringerVerlag, New York 1994.
A. Jacquin. “Image Coding Based on a Fractal Theory of Iterated Contractive Image Transformation∝, IEEE Transaction on Image Processing, 1992, Vol. 1, Nℴ 1.
C. Frigaard, J. Gade, T. T. Hemmingsen, T. Sand. “Image Compression Based on a Fractal Theory∝.
P. Aigrain, H. Zhang, D. Petkovic. “Content-based Representation and Retrieval of Visual Media: A State-of-the-Art Review∝, Multimedia Tools and Applications special issue on Representation and Retrieval of Visual Media
A. Pentland, R.W. Picard, S. Sclaroff. “Photobook: Content-Based Manipulation of Image Databases∝, International Journal of Computer Vision, Fall 1995
R. Mehrotra, J.E. Gary. “Similar-Shape Retrieval In Shape Data Management∝, Computer September 1995
W. Niblack, R. Barber, W. Equitz, M.D. Flickner, E. H. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, G. Taubin. “QBIC Project: querying images by content, using color, texture, and shape∝, Storage and Retrieval for Image and Video Databases
B. Scassellati, S. Alexopoulos, M.D. Flickner. “Retrieving images by 2D shape: a comparison of computation methods with human perceptual judgments∝, Storage and Retrieval for Image and Video Databases
D. Tegolo. “Shape analysis for image retrieval∝, Storage and Retrieval for Image and Video Databases
B.V. Funt and G.D. Finlayson. “Color constant color indexing∝, Technical report, School of Computing Science, Simon Fraser University, Vancouver, B.C. Canada 1991.
M.A. Stricker. “Color and geometry as cues for indexing∝, Technical report, Department of Computer Science, The University of Chicago, Nov. 1992
F. Arduini, S. Fioravanti, D. Giusto. “Natural Surface Characterization by Multifractals∝, MVA'90, IAPR Workshop on Machine Vision Applications, Nov 1990.
N. Sarkar, B.B. Chaudhuri. “An efficient approach to estimate fractal dimension of Textural Images∝, Pattern Recognition, Vol 25, No9, pp 1035–1041, 1992.
J.M. Marie-Julie — H. Essafi. “Fast parallel multimedia data base access based on wavelet multiresolution pyramidal decomposition∝, MVA'96, IAPR Workshop on Machine Vision Applications.
J.M. Marie-Julie — H. Essafi. “Digital Image Indexing and Retrieval by Content using the Fractal Transform for Multimedia Databases∝, to be published in ADL'97, IEEE Advance Digital Library, May 1997
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marie-Julie, J.M., Essafi, H. (1997). Image database indexing and retrieval using the Fractal Transform. In: Fdida, S., Morganti, M. (eds) Multimedia Applications, Services and Techniques — ECMAST '97. ECMAST 1997. Lecture Notes in Computer Science, vol 1242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037351
Download citation
DOI: https://doi.org/10.1007/BFb0037351
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63078-4
Online ISBN: 978-3-540-69126-6
eBook Packages: Springer Book Archive