Effective Frame work for Hierarchical Indexing Scheme using Expectation Maximization based on Full Automatic Algorithm

Abstract

The intersection method had a higher performance as shown by the ROC curves in our paper. We extended the EM-variant
algorithm to model each object as a Gaussian mixture, and the EM-variant extension outperforms the original EM-variant on the
image data set having generalized labels. Intersecting abstract regions was the winner in our experiments on combining two
different types of abstract regions. However, one issue is the tiny regions generated after intersection. The problem gets more
serious if more types of abstract regions are applied. Another issue is the correctness of doing so. In some situations, it may be
not appropriate to intersect abstract regions. For example, a line structure region corresponding to a building will be broken into
pieces if intersected with a color region. In future works, we attack these issues with two phase approach the classification
problem.