VICAL: Visual Cognitive Architecture for Concepts Learning to Understanding Semantic Image Content

摘要: In this paper, we are interested by the different sides of visual learning and machine learning, as well development ”visual cognitive” evolution cycle. For purpose, present an expected cognitive architecture framework to highlight all functionalities. Despite fact that our investigations were based on conception a processor high interpreter object recognition tasks, strongly emphasize novel evolutionary pyramidal learning. Indeed, elaborated approach association rules enables learn highest concepts induced from lower level in order progressively understand semantic content input image.