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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/652

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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