Gravitational transform for data clustering - application to multicomponent image classification

In this communication, we introduce the concept of gravitational transform, with application to multicomponent image classification. This general concept, from which many settings can be implemented, takes into account both feature sets extracted from the image and the spatial distance between pixels in order to further improve the classification, and thus the partitioning into homogeneous regions. Examples of classification and comparative results with synthetic data are presented, showing that this approach improves the classification rates in all cases, independently of the chosen classification technique, and outperforms some recent approaches in contextual multidimensional unsupervised clustering. The encouraging results obtained make this technique a valuable tool to insert between the feature extraction process and the unsupervised classification process.