EURASIP Journal on Applied Signal Processing 
Volume 2004 (2004), Issue 6, Pages 814-832
doi:10.1155/S1110865704401152

Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering

Fatih Porikli1 and Yao Wang2

 1Mitsubishi Electric Research Laboratories, Cambridge 02139, MA, USA
2Department of Electrical Engineering, Polytechnic University, Brooklyn 11201, NY, USA
Received 4 February 2003; Revised 26 December 2003

Abstract

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.