Video object encoder using region-of-interest based neural network classifiers

In this paper, a hybrid classifier is introduced which combines a linear discriminant classifier and a nonlinear non-parametric neural network based classifier such as the radial basis function neural networks. This hybrid model provides a linear parametric coding of the coarse-level information about the underlying image, and then uses the neural networks to encode the finer-level information of the same image. This model allows the selected image regions of interest be analyzed and encoded in the finer scales by a non-parametric neural network models whilst the image regions of no-interest are analyzed and encoded in coarse scales by a simple parametric model. The experiment on video image compression shows that the proposed model achieves significantly reduced computations for similar compression performance compared to other conventional methods.