Skip to main content
  • Research Article
  • Published:

Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering


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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Fatih Porikli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Porikli, F., Wang, Y. Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering. EURASIP J. Adv. Signal Process. 2004, 935786 (2004).

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI:

Keywords and phrases