Computing Polynomial Segmentation through Radial Surface Representation

The Visual Information Retrieval (VIR) area requires robust implementations achieved trough mathematical representations for images or data sets. The implementation of a mathematical modeling goes from the corpus image selection, an appropriate descriptor method, a segmentation approach and the similarity metric implementation whose are treated as VIR elements. The goal of this research is to found an appropriate modeling to explain how its items can be represented to achieve a better performance in VIR implementations. A direct method is tested with a subspace arrangement approach. The General Principal Component Analysis (GPCA) is modified inside its segmentation process. Initially, a corpus data sample is tested, the descriptor of RGB colors is implemented to obtain a three dimensional description of image data. Then a selection of radial basis function is achieved to improve the similarity metric implemented. It is concluded that a better performance can be achieved applying powerful extraction methods in visual image retrieval (VIR) designs based in a mathematical formulation. The results lead to design VIR systems with high level of performance based in radial basis functions and polynomial segmentations for handling data sets.