Summary
In this paper, we are interested by the different sides of the visual learning and the visual machine learning, as well as the development of the ”visual cognitive” evolution cycle. For this purpose, we present an expected cognitive architecture framework to highlight all the visual learning functionalities. Despite the fact that our investigations were based on the conception of a cognitive processor as a high interpreter of object recognition tasks, we strongly emphasize on a novel evolutionary pyramidal learning. Indeed, this elaborated learning approach based on association rules enables to learn highest concepts induced from concepts of lower level in order to progressively understand the highest semantic content of an input image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aguilar-Ruiz, J.S., Riquelme, J.C., Toro, M.: Evolutionary learning of hierarchical decision rules. IEEE Transactions on Man and Cybernetics, Part B 33(2), 324–331 (2003)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between set of items in large databases. In: Proc. ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large database. In: Proceedings of 20th International Conference on Very Large Data Bases, Santiago, Chile (1994)
Bhanu, B., Lin, Y., Krawiek, K.: Evolutionary synthesis of pattern recognition systems. Springer, New York (2005)
De Jong, K.A., Spearsm, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Machine Learning, 161–188 (1993)
Giordana, A., Neri, F.: Search intensive concept induction. Evolutionary Computation 3(4), 376–416 (1996)
Greene, D.P., Smith, S.F.: Competition-based induction of decision models from examples. Machine Learning 13, 229–257 (1993)
Hekanaho, J.: GA-based rule enhancement concept learning. In: Proc. Third International Conference on Knowledge Discovery and Data Mining, New Port Bearch, CA, USA, pp. 183–186 (1997)
Holland, J.: Escaping brittleness: The possibilities of general-purpose learning algorithms alied to parallel rule-based systems. In: Michalski, R., Carbonell, J., Mitchell, T. (eds.) Machine Learning: An Artificial Intelligence Aroach (1986)
Huttenlocher, D.P., Rucklidge, W.J.: A multi-resolution technique for comparing images using Hausdorff distance, Dpt. of CS, Cornell University (1994)
Janikow, C.Z.: A knowledge intensive genetic algorithm for supervised learning. Machine Learning 13, 189–228 (1993)
Krawiec, K.: Learning high-level visual concepts using attributed primitives and genetics programming. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 515–519. Springer, Heidelberg (2006)
Krawiec, K.: Evolutionary learning of primitive-based visual concepts. In: Proc. IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 1308–1315 (2006)
Krawiec, K., Bhanu, B.: Visual learning by coevolutionary features synthesis. IEEE Transactions on Systems Man and Cybernetics Part B 35, 409–425 (2005)
Maloof, M., Langley, P., Binford, P., Nevatia, R., Sage, S.: Improved rooftop detection in aerial images with machine learning. Machine Learning 53, 157–1991 (2003)
Ogiela, M., Tadeusiewicz, R.: Nonlinear processing and semantic content analysis in medical imaging - a cognitive aroach. IEEE Transactions on Instrumentation and Measurements 54, 2149–2155 (2005)
Rizki, M., Zmuda, M., Tamburino, L.: Evolving pattern recognition systems. IEEE transactions on Evolutionary Computation 6, 594–609 (2002)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules. In: Proceedings of the VLDB Conference, Zurich, Switzerland, pp. 432–444 (1995)
Segen, J.: GEST: a learning computer vision system that recognizes hand gestures. In: Michalski, R., Tecuci, G. (eds.) Machine Learning, A Multistrategy Aroach, vol. IV, pp. 621–634. Morgan Kaufman, San Francisco (1994)
Smith, S.: Flexible learning of problem solving heuristics through adaptive search. In: Proc. Eight International Joint Conference on Artificial Intelligence, pp. 422–425 (1983)
Teller, A., Veloso, M.: PADO: a new learning architecture for object recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 77–112. Oxford Press, New York (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ali, Y.M.B. (2010). VICAL: Visual Cognitive Architecture for Concepts Learning to Understanding Semantic Image Content. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 2. Advances in Intelligent and Soft Computing, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16295-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-16295-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16294-7
Online ISBN: 978-3-642-16295-4
eBook Packages: EngineeringEngineering (R0)