Enhanced ICA mixture model for image segmentation

The ICA mixture model has been proposed to perform unsupervised classification q data modelled as a mixture of classes described by linear combinations of independent, non-Gaussian densities. Since the original learning algorithm is based on a gradient optimization technique, it was noted that its performance is affected by some known limitations associated with this kind of approach. In this paper improvements based on implementation and modelling aspects are incorporated to ICA mixture model aiming to apply it for image segmentation. Comparative experimental results obtained by the enhanced method and the original one are presented to show that the proposed modifications can significantly improve the classification and segmentation performance considering random generated data and some image data of public domain.