Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Hierarchical Selectivity for Object-Based Visual Attention
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Relevance in spatial navigation and communication
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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Knowledge-based clustering and autonomous mental development remains a high priority research topic, among which the learning techniques of neural networks are used to achieve optimal performance. In this paper, we present a new framework that can automatically generate a relevance map from sensory data that can represent knowledge regarding objects and infer new knowledge about novel objects. The proposed model is based on understating of the visual what pathway in our brain. A bottom-up attention model can selectively decide salient object areas. Color and form features for a selected object are generated by a sparse coding mechanism by a convolution neural network (CNN). Using the extracted features by the CNN as inputs, the incremental knowledge representation model, called the growing fuzzy topology adaptive resonant theory (TART) network, makes clusters for the construction of an ontology map in the color and form domains. The clustered information is relevant to describe specific objects, and the proposed model can automatically infer an unknown object by using the learned information. Experimental results with real data have demonstrated the validity of this approach.