A Neural-Network-Based Approach to Adaptive Human Computer Interaction
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Adaptable Neural Networks for Unsupervised Video Object Segmentation of Stereoscopic Sequences
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Neural Networks Retraining for Unsupervised Video Object Segmentation of Videoconference Sequences
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Human action analysis, annotation and modeling in video streams based on implicit user interaction
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Human action annotation, modeling and analysis based on implicit user interaction
Multimedia Tools and Applications
Enhanced human behavior recognition using HMM and evaluative rectification
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Adaptive on-line neural network retraining for real life multimodal emotion recognition
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Mining movie archives for song sequences
Multimedia Tools and Applications
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A novel approach is presented in this paper for improving the performance of neural-network classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network weights to the current condition; 2) a maximum a posteriori (MAP) estimation procedure for optimally selecting the most representative data of the current environment as retraining data; and 3) a decision mechanism for determining when network retraining should be activated. The training algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization. The MAP estimation procedure models the network output as a Markov random field (MRF) and optimally selects the set of training inputs and corresponding desired outputs. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in real-life experiments