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

F. M. Porikli, Y. Wang

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish (US)
Number of pages4
StatePublished - 2001
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001


OtherIEEE International Conference on Image Processing (ICIP)

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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