A High Dimensional Framework for Joint Color-Spatial Segmentation

This paper deals with region-of-interest (ROI) segmentation in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. Color and geometry can be combined in a joint PDF. However such high-dimensional PDFs being hard to estimate, measures based on PDF distances may lead to incorrect segmentations. Here, we propose to use an estimate of the Kullback-Leibler divergence adapted to high-dimensional PDFs. It is defined from the samples using the kth-nearest neighbor (kNN) framework and it is differentiated for active contour implementation and expressed in both the continuous form and a kNN form. Results are presented on standard sequences.