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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/652
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| Title: | Compound Markov Random Field Model Based Video Segmentation |
| Authors: | Subudhi, B N
Nanda, P K |
| Keywords: | Covariance matrices
Feature extraction
Gaussian distribution
Gaussian process
Image edge analysis
Image segmentation
pattern recogntion
Simulated Annealing |
| Issue Date: | 2008 |
| Publisher: | SPIT-IEEE |
| Citation: | Proceedings of SPIT-IEEE Colloquium and International Conference, 4-5 February 2008, Sardar Patel Institute of Technology, Mumbai, India Vol 1, P 97-102 |
| Abstract: | We present a novel approach of video segmentation
using the proposed compound Markov Random Field video
model. This segmentation scheme is based on the spatio-temporal
approach where one MRF model is used to model the spatial
image and other two MRF models take care in the temporal
directions. In this modeling, edge feature in the temporal
direction has been introduced to preserve the edges in the
segmented images. The problem is formulated as pixel labeling
problem and the pixel labels are estimated using the Maximum a
Posteriori (MAP) criterion. The MAP estimates are obtained by
the proposed hybrid algorithm. The performance of the proposed
method is found to be better than that of JSEG method in terms
of percentage of misclassification. Different examples are
presented to validate the proposed approach. |
| Description: | Copyright for the paper belongs to Proceedings Publisher |
| URI: | http://hdl.handle.net/2080/652 |
| Appears in Collections: | Conference Papers
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| spit-82.pdf | | 396Kb | Adobe PDF | View/Open |
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