Skip to content


  • Research Article
  • Open Access

Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering

EURASIP Journal on Advances in Signal Processing20042004:935786

  • Received: 4 February 2003
  • Published:


We introduce an automatic segmentation framework that blends the advantages of color-, texture-, shape-, and motion-based segmentation methods in a computationally feasible way. A spatiotemporal data structure is first constructed for each group of video frames, in which each pixel is assigned a feature vector based on low-level visual information. Then, the smallest homogeneous components, so-called as volumes, are expanded from selected marker points using an adaptive, three-dimensional, centroid-linkage method. Self descriptors that characterize each volume and relational descriptors that capture the mutual properties between pairs of volumes are determined by evaluating the boundary, trajectory, and motion of the volumes. These descriptors are used to measure the similarity between volumes based on which volumes are further grouped into objects. A fine-to-coarse clustering algorithm yields a multiresolution object tree representation as an output of the segmentation.

Keywords and phrases

  • video segmentation
  • object detection
  • centroid linkage
  • color similarity

Authors’ Affiliations

Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA
Department of Electrical Engineering, Polytechnic University, Brooklyn, NY 11201, USA


© Porikli and Wang 2004