Evolving fuzzy neural network for camera operations recognition
Irena Koprinska, Nikola Kirilov Kasabov · 2002
Reports an application of an evolving fuzzy neural network (EFuNN) for camera operations recognition. EFuNN features one-pass learning, dynamical growing and shrinking architecture and ability to accommodate new knowledge without the need to retrain the network on both the original and new data. The network learns from pre-classified examples in the form of motion vector patterns, extracted from MPEG compressed video, in order to distinguish between six classes: static, panning, zooming, object motion, tracking and dissolve. The performance of EFuNN is compared with LVQ and the results are discussed. In addition, the impact of the number of membership functions and the contribution of the rule node aggregation are analyzed.