The week's pick
Random Articles
Reseach Article
Feature Selection in Top-Down Visual Attention Model using WEKA
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 24 - Number 4 |
| Year of Publication: 2011 |
| Authors: Amudha.J, Soman.K.P, Kiran.Y |
10.5120/2955-3895
|
Amudha.J, Soman.K.P, Kiran.Y . Feature Selection in Top-Down Visual Attention Model using WEKA. International Journal of Computer Applications. 24, 4 ( June 2011), 38-43. DOI=10.5120/2955-3895
Abstract
A feature selection in Top down visual attention model for sign board recognition has been incorporated to reduce the computational complexity and to enhance the quality of recognition. The approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on the concepts of the human visual system. A top-down guided visual search module of the system identifies the most discriminate feature from the previously learned target object and uses to recognize the object. This enables a significantly faster classification and is illustrated in identifying signboards in a road scene environment.
References
- Amudha.J, Soman K.P, Vasanth. K, “Video Annotation Using Saliency”, International conference on Image processing, Computer vision and Pattern Recognition” Vol 1, 191-195, 2008.
- Amudha J and Soman K.P., “Selective tuning Visual Attention Model” International Journal of Recent Trends in Engineering, November 2009, Finland.
- Amudha J and Soman K.P., “Saliency based Visual Tracking of vehicles” International Journal of Recent Trends in Engineering, November 2009 Issue, Finland.
- Breiman, L., Random Forests, Machine Learning, 45(1), 5-32, 2001
- Breiman, L., Friedman, J,H., Olshen, R.A., and Stone, C.J. , Classification and Regression trees, Wadsworth, Belmont, CA(1984)
- Ian H. Witten; Eibe Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann, San Francisco. 2005
- L.Itti, C. Koch, and E. Niebur, ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259, 1998
- Koch.C and Ullman.S, “Shifts in selective visual attention: towards the underlying neural circuitry”, Human Neurobiology, vol. 4, pp. 219-227, 1985.
- Lewis, R.J. An Introduction to Classification and Regression Tree (CART) Analysis. 2000 Annual Meeting of the Society for Academic Emergency Medicine, Francisco, California 2000.
- Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.. Decision trees: an overview and their use in medicine, Journal of Medical Systems Kluwer cademic/Plenum Press, vol.26, Num. 5, pp. 445-463, 2002.
- Shi.H. Best-first decision tree learning. M.Sc. Thesis, University of Waikato, Hamilton, NZ.
- Simone Frintrop and Erich Rome, “Simulating Visual Attention for Object Recognition” Fraunhofer Institute for Autonomous Intelligent Systems (AIS) Schloss Birlinghoven, D-53754 Sankt Augustin, Germany
- Simone Frintrop, “VOCUS: a visual attention system for object detection and goal-directed search,” Ph.D. dissertation, Rheinische Friedrich- Wilhelms-University, Bonn, Germany, July 2005, published 2006 in Lecture Notes in Artificial Intelligence (LNAI), Vol. 3899, Springer Verlag Berlin/Heidelberg.
- Simone Frintrop, Patric Jensfelt, and Henrik Christensen. Pay attention when selecting features. In Proc. Int’l Conf. on Pattern Recognition (ICPR 2006), 2006.
- Simone Frintrop, Patric Jensfelt, and Henrik Christensen. Attentional robot localization and mapping. In ICVS Workshop on Computational Attention & Applications (WCAA), 2007.
- Simone Frintrop, Maria Klodt, and Erich Rome. A real-time visual attention system using integral images. In Proc. of Int’l Conf. on Computer Vision Systems (ICVS), 2007.
- Theeuwes.J, Top Down Searhc Strategies Cannot Override Attetnional Capture. Psychonomic Bulletin and Review, 11:65-70, 2004.
Index Terms
Keywords