An adaptive clustering algorithm for segmentation of video sequences

We present a Bayesian approach for segmenting a sequence of gray-scale images to obtain a binary sketch. We extend a 2-D algorithm to video sequences. The 2-D algorithm is an adaptive thresholding scheme that uses spatial constraints and takes into consideration the local intensity characteristics of the image. We model the segmentation distribution as a 3-D Gibbs random field. We add temporal constraints and temporal local intensity adaptation to ensure a smooth transition of the segmentation from frame to frame. For computational efficiency as well as performance we use a multi-resolution approach. We also consider several suboptimal implementations to reduce the delay as well as the amount of computation. We tested the performance of the algorithm on head and shoulders video sequences. The algorithm achieves accurate rendering of the lip and eye movements and preserves the main characteristics of the face, so that it is easily recognizable