Abstract
In this paper we face the problem of finding characteristic information about. images of different objects, showing that the fractal encoding based on Iterated Function Systems, besides allowing very high compression rates, can be successfully applied also for capturing discriminatory features that can be exploited for non-fractalimage classification. An original feature extraction algorithm was developed and applied to encode the hand-written digits data set. Then, different learning algorithms were applied and their performances were compared both to those obtained using a general purpose fractal encoder (enc by Fisher and to the work done in the StatLog project on the same data set.
Preview
Unable to display preview. Download preview PDF.
References
ECLiPSe3.5 Extensions User Manual. ECRC GmbH, 1995.
R. Anand, K. Mehrotra, C. K. Mohan, and S. Ranka. Analyzing images containing multiple sparse pattern with neural networks. In Proc. of IJCAI-91, Sidney, Australia, 1991.
M. Barnsley. Fractals Everywhere. Academic Press, San Diego, 1988.
M. Barnsley and S. Demko. Iterated function systems and the global construction of fractals. In The Proceedings of the Royal Society of London, volume A399, pages 343–275, 1985.
M. Barnsley and L. P. Hurd. Fractal Image Compression. AK Peters, Ltd., Wellesley, Massachusetts, 1993.
P. Besl and R. Jain. Three dimensional object recognition. ACM Computing Surveys, (17):75–154, 1985.
L. Breiman, J. Friedman, J. Ohlsen, and C. Stone. Classification and Regression Trees. Wadsworth & Brooks, Pacific Grove, CA, 1984.
G. Le Chiara and L. Saitta. Using fractals to learn image descriptions by means of artificial neural networks. In IEEE International Conference on Neural Networks, Orlando, USA, 1994.
R. Chin and C. Dyer. Model-based recognition in robot vision. ACM Computing Surveys, (18):67–108, 1986.
Y. Fisher (Ed.). Fractal Compression: Theory and Application to Digital Images. Springer Verlag, New York, 1994.
K. Falconer. Fractal Geometry, Mathematical Foundations and Applications. John Wiley & Sons Ltd., Chichester, UK, 1990.
D.E. Goldberg. Genetic Algorithms. Addison-Wesley, Readings, MA, 1989.
V. K. Govindan and A. P. Shivaprasad. Character recogniton — A review. Pattern Recognition, 23(7):671–683, 1990.
W. Grimson, Lozano-Pérez, and D. Huttenlocher. Recognition by Computer: The Role of Geometric Constraints. The MIT Press, Cambridge, MA, 1990.
D. Huttenlocher, G. Klanderman, and W. Rucklidge. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Analysis and Machine Intelligence, (PAMI-15):850–863, 1993.
A. Jacquin. Image coding based on Factal theory of iterated contractive image transforms. In Proc. of SPIE, Visual Communications and and Image Processing '90, volume 1360, 1990.
B. Mandelbrot. The Fractal Geometry of Nature. Freeman & Co., San Francisco, CA, 1982.
D. Michie, D. J. Spiegelhalter, and C. C. Taylor. Machine learning, neural and statistical classification. Ellis Horwood series in artificial intelligence. Prentice Hall, 1994.
F. Neri and A. Giordana. A distributed genetic algorithm for concept learning. In Int. Conf. on Genetic Algorithms, pages 436–443, Pittsburgh, PA, 1995. Morgan Kaufmann.
D. Oliver. Fractal Vision, Put Fractals to Work for You. Sams Publishing, Indiana, USA, 1992.
A. P. Pentland. Fractal-Based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6):661–674, 1984.
A. Rosenfeld and A. Kak. Digital Picture Processing. Academic Press, New York, NY, 1982.
P. D. Wasserman. Neural computing. 1995.
C. J. Wu and J. S. Huang. Human face profile recognition by computer. Pattern Recognition, 23(3/4):255–259, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baldoni, M., Baroglio, C., Cavagnino, D., Lo Bello, G. (1997). Extraction of discriminant features from image fractal encoding. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_102
Download citation
DOI: https://doi.org/10.1007/3-540-63576-9_102
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63576-5
Online ISBN: 978-3-540-69601-8
eBook Packages: Springer Book Archive