Genetic programming for primitive-based acquisition of visual concepts

• Autorzy: Krawiec K.
• Konferencja: Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
• Języki publikacji: EN

Abstrakty

EN

We describe a novel method for acquisition of higher-level visual concepts using GP-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: individuals-learners are rewarded for being able to restore the essential features (here: shape) of the visual stimulus. The approach is general and does not require any a priori knowledge about the particular application or target concept to be learned; the only prerequisite is universal knowledge related to interpretation of visual information, encoded in nodes of GP trees. The paper demonstrates the performance of the method on a specific visual task of acquiring the concept of a triangle from examples given in a form of raw raster images.

Słowa kluczowe

EN

genetic programming; pattern recognition; computer system; visual learning

PL

programowanie genetyczne; system komputerowy; uczenie wzrokowe; rozpoznawanie obrazów

• Wydawca: Oficyna Wydawnicza Politechniki Warszawskiej
• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2006
• Tom: z. 156
• Strony: 211--222
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Krawiec K.

• Poznań University of Technology, Institute of Computing Science, Poznań, Poland,

Bibliografia

• [1] Draper, B., Hanson, A., Riseman, E. (1993) "Learning blackboard-based scheduling algorithms for computer vision", International Journal of Pattern Recognition and Artificial Intelligence, vol. 7, 309-328, March.
• [2] Johnson, M.P., Maes, P., Darrell, T. (1994) "Evolving visual routines", in: R.A. Brooks, P. Maes (red.) Artificial Life IV: proceedings of the fourth international workshop on the synthesis and simulation of living systems, Cambridge, MA: MIT Press, 373-390.
• [3] Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.A. (1999) Genetic Programming III: Darwinian Invention and Problem Solving, San Francisco, CA: Morgan Kaufman.
• [4] Krawiec, K., Bhanu, B.: Visual Learning by Coevolutionary Feature Synthesis, IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics 35 (2005) 409-425.
• [5] Luke, S. (2002) "ECJ Evolutionary Computation System", http://www.cs.umd.edu/projects/plus/ec/ecj/
• [6] Maloof, M.A., Langley, P., Binford, T.O., Nevatia, R., Sage, S. (2003) "Improved roof-top detection in aerial images with machine learning", Machine Learning, vol. 53, 157-191.
• [7] Marek, A., Smart, W.D. and Martin, M.C. (2003) "Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming", In Proceedings of the IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Madison, WI.
• [8] Marr, D. (1982) Vision, W.H. Freeman, San Francisco, CA.
• [9] Rizki, M., Zmuda, M., Tamburino, L. (2002) "Evolving pattern recognition systems", IEEE Transactions on Evolutionary Computation, vol. 6, 594-609.
• [10] Segen, J. (1994) "GEST: A learning computer vision system that recognizes hand gestures", In: R.S. Michalski and G. Tecuci (ed.) Machine learning. A Multistrategy Approach, Volume IV, San Francisco, CA: Morgan Kaufmann, 621-634.
• [11] Sun Microsystems, Inc., (2001) "Java Advanced Imaging API Specification", Version 1.2.
• [12] Teller, A., Veloso, M.M. (1997) "PADO: A new learning architecture for object recognition", In: K. Ikeuchi and M. Veloso (ed.) Symbolic Visual Learning, Oxford Press, 77-112.
• [13] Torralba, A., Murphy, K.M., Freeman, W.T. (2004) "MIT-CSAIL Computer vision annotated image library", http://web.mit.edu/torralba/www/database.html