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Visual information fusion for object-based video image segmentation using unsupervised Bayesian online learning

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3 Author(s)
Jia, Z. ; United Technol. Res. Center, Shanghai ; Balasuriya, A. ; Challa, S.

An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, different visual features (the spatial location, colour and optical-flow vectors) are fused in a probability framework for image pixel clustering. The appropriate modelling of the probability distribution function (PDF) for each feature-cluster is obtained through a Gaussian distribution. The image pixel is then assigned a cluster number in a maximum a posteriori probability framework. Different from the previous segmentation methods, the unsupervised Bayesian online learning algorithm has been developed to understand a cluster's PDF parameters through the image sequence. This online learning process uses the pixels of the previous clustered image and information from the feature-cluster to update the PDF parameters for segmentation of the current image. The unsupervised Bayesian online learning algorithm has shown satisfactory experimental results on different video sequences.

Published in:

Image Processing, IET  (Volume:1 ,  Issue: 2 )