Semi-automatic object-based video segmentation with labeling of color segments

摘要

In this paper we propose a semi-automatic method for general object-based segmentation of image sequences. A label field is initialized by the user in the first frame of the sequence and then it is automatically tracked for the rest of the frames based on the color and motion properties of the various objects in the scene. We propose a novel statistical modeling which is based on the local objects’ properties. The locality introduced in our modeling allows the tracking of complex objects that, globally, might be inhomogeneous in their color and motion characteristics. The labeling criterion is the maximization of the joint probability of the labels and the observed color and motion properties. The proposed method utilizes an initial color-based segmentation which is obtained for each frame of the sequence. Both the modeling and the optimization are expressed in terms of the color-segments’ statistical properties. Experimental results are presented on real-image sequences that include complex human motion in cluttered backgrounds.