Knowledge Representation and Reasoning
Knowledge Representation and Reasoning
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Approaches and Challenges for Cognitive Vision Systems
Creating Brain-Like Intelligence
Demand-Driven Visual Information Acquisition
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Relevance in spatial navigation and communication
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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A fast and reliable visual search is crucial for representing visual scenes. The modulation of bottom-up attention plays an important role here. The knowledge about target features is often used to bias the bottom-up pathway. In this paper we propose a system which does not only make use of knowledge about the target features, but also uses already acquired knowledge about objects in the current scene to speed up the visual search. Main ingredients are a relational short term memory in combination with a semantic relational long term memory and an adjustable bottom-up saliency. The focus of this work is to investigate mechanisms to use the memory of the system efficiently. We show a proof-of-concept implementation working in a real-world environment and performing visual search tasks. It becomes clear that using the relational semantic memory in combination with spatial and feature modulation of the bottom-up path is beneficial for speeding up such search tasks.