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Published in:
2010 | OriginalPaper | Chapter
VICAL: Visual Cognitive Architecture for Concepts Learning to Understanding Semantic Image Content
Author : Yamina Mohamed Ben Ali
Published in: Image Processing and Communications Challenges 2
Publisher: Springer Berlin Heidelberg
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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.