First, to learn the models of scene structures. A traffic scene is segmented into local semantic regions by exploiting the temporal cooccurrence of local motions. Second,to cluster trajectories of feature points into objects and to estimate average
In this work, we propose a probabilistic framework that models how and which local regions from an image may be forgotten using a data-driven approach that combines local and global images features.
A novel unsupervised approach for regions of interest (ROI) extraction that combines the modified visual attention model and clustering analysis method is proposed. Then the non-uniform color image compression algorithm is followed to compress ROI an
