VICAL: Visual Cognitive Architecture to Understanding Image Semantic Content
DOI: 10.1007/978-3-642-15211-5_33
关键词:
摘要: In this paper, we are interested by the different sides of visual machine learning. For purpose, present an expected cognitive architecture to highlight all learning functionalities. Despite fact that our investigations were based on conception a processor, as high interpreter object recognition tasks, strongly emphasize novel evolutionary pyramidal Indeed, elaborated approach association rules enables learn highest concepts in order understand semantic content input image.
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Stephen F. Smith, Flexible learning of problem solving heuristics through adaptive search international joint conference on artificial intelligence. pp. 422- 425 ,(1983)
Katsushi Ikeuchi, Manuela Veloso, Symbolic visual learning Symbolic visual learning. pp. 355- 355 ,(1997)
M.A. Maloof, P. Langley, T.O. Binford, R. Nevatia, S. Sage, Improved Rooftop Detection in Aerial Images with Machine Learning Machine Learning. ,vol. 53, pp. 157- 191 ,(2003) , 10.1023/A:1025623527461
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
Anikó Ekárt, Mario Giacobini, Anna Isabel Esparcia-Alcázar, Stefano Cagnoni, Anthony Brabazon, Muddassar Farooq, Penousal Machado, Gianni A. di Caro, Andreas Fink, Applications of Evolutionary Computing ,(2008)
Krzysztof Krawiec, Bir Bhanu, Yingqiang Lin, Evolutionary Synthesis of Pattern Recognition Systems ,(2005)
Manuela Veloso, Astro Teller, PADO: a new learning architecture for object recognition Symbolic visual learning. pp. 77- 112 ,(1997)
J. G. Carbonell, T. M. Mitchell, R. S. Michalski, Machine Learning: An Artificial Intelligence Approach Springer Publishing Company, Incorporated. ,(2013)
Krzysztof Krawiec, Learning high-level visual concepts using attributed primitives and genetic programming Lecture Notes in Computer Science. pp. 515- 519 ,(2006) , 10.1007/11732242_48
Shamkant B. Navathe, Edward Omiecinski, Ashoka Savasere, An Efficient Algorithm for Mining Association Rules in Large Databases very large data bases. pp. 432- 444 ,(1995)