An Attention Selection System Based on Neural Network and Its Application in Tracking Objects
In this paper an attention selection system based on neural network is proposed, which combines supervised and unsupervised learning reasonably. A value system and memory tree with update ability are regarded as teachers to adjust the weights of neural network. Both bottom-up and top-down part are to simulate two-stage hypothesis of attention selection in biological vision. The system is able to track objects that it is interested in. Whenever it lost focus on tracked object, it can find the object again in a short time.
KeywordsLeaf Node Connection Weight Unsupervised Learning Gabor Filter Social Robot
Unable to display preview. Download preview PDF.
- Breazeal, C., Scassellati, B.: A Context-Dependent Attention System for a Social Robot. In: Proc. Int. Joint Conf. Artificial Intelligence, pp. 1146–1151 (1999)Google Scholar
- Weng, J., Zhang, Y., Hwang, W.: Incremental Hierarchical Discriminant Regression for Online Image Classification. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, September 10-13, pp. 476–480 (2001)Google Scholar