International Journal of Computer Vision
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
Special Issue On Representation And Coding Of Images And Video I [Guest Editorial]
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation for content-based coding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
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In this paper efficient performance generalization of neural network classifiers is accomplished, for unsupervised video object segmentation in videoconference/videophone sequences. Each time conditions change, a retraining phase is activated and the neural network classifier is adapted to the new environment. During retraining both the former and current knowledge are utilized so that good network generalization is achieved. The retraining algorithm results in the minimization of a convex function subject to linear constraints, leading to very fast network weight adaptation. Current knowledge is unsupervisedly extracted using a face-body detector, based on Gaussian p.d.f models. A binary template matching technique is also incorporated, which imposes shape constraints to candidate face regions. Finally the retrained network performs video object segmentation to the new environment. Several experiments on real sequences indicate the promising performance of the proposed adaptive neural network as efficient video object segmentation tool.