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Visual Bootstrapping for Unsupervised Symbol Grounding

Kittler, J, Shevchenko, M and Windridge, D (2006) Visual Bootstrapping for Unsupervised Symbol Grounding

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Most existing cognitive architectures integrate computer vision and symbolic reasoning. However, there is still a gap between low-level scene representations (signals) and abstract symbols. Manually attaching, i.e. grounding, the symbols on the physical context makes it impossible to expand system capabilities by learning new concepts. This paper presents a visual bootstrapping approach for the unsupervised symbol grounding. The method is based on a recursive clustering of a perceptual category domain controlled by goal acquisition from the visual environment. The novelty of the method consists in division of goals into the classes of parameter goal, invariant goal and context goal. The proposed system exhibits incremental learning in such a manner as to allow effective transferable representation of high-level concepts.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Shevchenko, M
Date : September 2006
Contributors :
Blanc-Talon, J
Philips, W
Popescu, D
Scheunders, P
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:38
Last Modified : 19 Dec 2019 00:29

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