A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Biologically Inspired Saliency Map Model for Bottom-up Visual Attention
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
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multitask learning model for online pattern recognition
IEEE Transactions on Neural Networks
A vector quantization approach for life-long learning of categories
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A neural network model for sequential multitask pattern recognition problems
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Dynamic obstacle identification based on global and local features for a driver assistance system
Neural Computing and Applications - Special Issue on ICONIP2009
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This paper presents an adaptive object recognition model based on incremental feature representation and a hierarchical feature classifier that offers plasticity to accommodate additional input data and reduces the problem of forgetting previously learned information. The incremental feature representation method applies adaptive prototype generation with a cortex-like mechanism to conventional feature representation to enable an incremental reflection of various object characteristics, such as feature dimensions in the learning process. A feature classifier based on using a hierarchical generative model recognizes various objects with variant feature dimensions during the learning process. Experimental results show that the adaptive object recognition model successfully recognizes single and multiple-object classes with enhanced stability and flexibility.