Mining Image Features in an Automatic Two-Dimensional Shape Recognition System
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:49825", author = "R. A. Salam and M.A. Rodrigues", title = "Mining Image Features in an Automatic Two-Dimensional Shape Recognition System", 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.", volume = "2", number = "1", pages = "13-7", }