Mining Image Features in an Automatic Two-Dimensional Shape Recognition System

Authors: R. A. Salam, M.A. Rodrigues

Abstract:

The number of features required to represent an image can be very huge. Using all available features to recognize objects can suffer from curse dimensionality. Feature selection and extraction is the pre-processing step of image mining. Main issues in analyzing images is the effective identification of features and another one is extracting them. The mining problem that has been focused is the grouping of features for different shapes. Experiments have been conducted by using shape outline as the features. Shape outline readings are put through normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. After this pre-processing step data will be grouped through their shapes. Through statistical analysis, these readings together with peak measures a robust classification and recognition process is achieved. Tests showed that the suggested methods are able to automatically recognize objects through their shapes. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion.

Keywords: Image mining, feature selection, shape recognition, peak measures.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328882

References:

[1] J. Zhang, W. Hsu, and M. L. Lee, An Information-driven Framework for Image Mining, in Proceedings of the 12th International Conference on Database and Expert Systems Applications (DEXA), Munich, German, 2001.
[2] I. Bierderman, and G. Ju, Surface vs. Edge-based Determinants of Visual Recognition. Cognitive Psychology, 20, 38-64, 1988.
[3] W. G. Hayward, Effects of Outline Shape in Object Recognition. Journal of Experimental psychology: Human Perception and Performance, 24(2), 427-440, 1988.
[4] I. Taylor and M. M. Taylor, The Psychology of Reading. London and New York Academic Press, 1983.
[5] I. Rock, F. Halper, T. Clayton, The Perception and Recognition of Complex Figures. Cognitive Psychology, 3, 655-673, 1972.
[6] R. N. Haber, R. Haber, Visual components of the Reading Process. Visible Language, 15, 147-182, 1981.
[7] R. G. Crowder, The Psychology of Reading. Oxford University Press, 1982.
[8] A. Jain, A. Vailaya, Image Retrieval using Color and Shape, Pattern Recognition, 29(8), 1233-1244, 1996.
[9] W. Ma, Y. Deng, and B. S, Manjunath, Tools for Texture/Color Based Search of Images, SPIE International Conference, Human Vision and Electronic Imaging, 497-507, 1997.
[10] K. Schulten, The Development of the Primary Visual Cortex. Theoretical Biophysics Group, Beckman Institute, University of Ilionis, USA, Available : http://www.ks.uiuc.edu/Research/Neural/development. html, (16th September 2002).
[11] Z. Pylyshyn, Is Vision Continuous with Cognition? - The Case for Cognitive Impenetrability of Visual Perception. Technical Report TR- 38, 1998, Rutgers Center for Cognitive Science, Rutgers University, New Brunswick, NJ, Available: http://ruccs.rutgers.edu/ publicationsreports.html
[12] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active Models. International Journal of Computer Vision, 321-331, 1988.
[13] R. P. Grzeszczuk and D. N. Levin, Brownian Strings: Segmenting Images with Stochastically Deformable s. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1100-1114, 1997.
[14] H. Mulholand and C. R. Jones, Fundamental of Statistics. London Butterworths, London, 1968.