An unsupervised multi-resolution object extraction algorithm using video-cube

We propose a fast video object segmentation method that detects object boundaries accurately, and does not require any user assistance. Video streams are considered as 3D data, called video-cubes, to take advantage of 3D signal processing techniques. After a video sequence is filtered, marker nodes are selected from the color gradient. A volume around each marker is grown by using color/texture distance criteria. Then volumes that have similar characteristics are merged. Self-descriptors for each volume, mutual-descriptors for each pair of volumes are computed. These descriptors capture motion and spatial information of volumes. In the clustering stage, volumes are classified into objects in a fine-to-coarse hierarchy. While applying and relaxing descriptor based adaptive, similarity scores are estimated for each possible pair-wise combination of volumes. The pair that gives the maximum score is clustered iteratively. Finally, an object-based multi-resolution representation tree is assembled.