A color-based classifier for region identification in video

Content based coding has been proposed by several authors and members of the MPEG-4 community as a solution to very low rate video coding. Using content coding, a video sequence is decomposed into objects that may be encoded independently. Such a scheme requires a fast and accurate segmentation algorithm to identify various objects in the scene. In this paper we propose, develop, and analyze a color-based segmentation algorithm. One application of interest is coding of sign language video sequences. The requirements for accurate perception of sign language differ from those of traditional head-and-shoulders videoconferencing sequences. We propose a content-based coding method in which perceptually important regions in an image are identified, and more resources are allocated to these regions. Since face, hands and arms are important components of sign language, regions are defined that encompass these features. The dynamic segmentation algorithm identifies flesh regions using statistical methods operating on image color distributions. A method for performing the segmentation in the perceptually Linear L*a*b* space using data captured in the YCbCr space is developed. Results of encoding sign language sequences using the proposed content-based methods illustrate the improved quality that can be achieved at a the same bit rate when compared to a uniform algorithm.