Mean shift clustering based outlier removal for global motion estimation

Abstract

This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.

Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

“…They developed a dimension-decreasing method that protected the geometric structure of the shape because high dimension affects the clustering performance in a negative way [5]. Okade and Biswas suggested ''a new motion vector outlier rejection method on the basis of using mean shift clustering on block motion vectors" [6].…”