In this paper, we present a perception principlesguided video segmentation method, where statistical modeling and graph-theoretic approaches are combined in a multi-layer classification architecture. Various visual cues are effectively incorporated in a sequential segmentation process. Specifically, low-level pixel-wise features are used in the first layer where a joint spatio-temproal statistical modeling approach is used to construct entry-level visual units in space-time. In the second

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... all units are first classified into dynamic or static units based their motion magnitudes. Then dynamic units are further parsed into over-segmented moving regions that are connected in space and time, and a mid-level feature, motion trajectory, is extracted for each moving region. In the third layer, still and moving regions are merged into background and moving objects by a graph-based approach with different similarity metrics. The proposed algorithm employs both long-range motion information, i.e., trajectory, and short-range motion information, i.e., change detection, to retain temporal continuity and spatial homogeneity of moving objects. The proposed multi-layer structure ensembles the joint spatio-temproal and cascade process of perception principles and support efficient and accurate object segmentation.