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Visual Schemas in Neural Networks for Object Recognition and Scene Analysis

Leow, Wee Kheng and Miikkulainen, Risto (1997) Visual Schemas in Neural Networks for Object Recognition and Scene Analysis. [Journal (Paginated)]

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Abstract

VISOR is a large connectionist system that shows how visual schemas can be learned, represented, and used through mechanisms natural to neural networks. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Simulations show that VISOR is robust against noise and variations in the inputs and parameters. It can indicate the confidence of its analysis, pay attention to important minor differences, and use context to recognize ambiguous objects. Experiments also suggest that the representation and learning are stable, and its behavior is consistent with human processes such as priming, perceptual reversal, and circular reaction in learning. The schema mechanisms of VISOR can serve as a starting point for building robust high-level vision systems, and perhaps for schema-based motor control and natural language processing systems as well. </blockquote>

Item Type:Journal (Paginated)
Keywords:visual schemas, schema learning, priming, perceptual reversal, circular reaction, schema hierarchy, hierarchical learning, robustness
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Vision
Computer Science > Neural Nets
Psychology > Perceptual Cognitive Psychology
ID Code:526
Deposited By:Miikkulainen, Risto
Deposited On:03 Jan 1999
Last Modified:11 Mar 2011 08:54

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