Unsupervised evaluation method using Markov random field for moving object segmentation in infrared videos. Issue 7 (1st July 2014)

• Record Type: Journal Article
• Authors: Min, Chaobo; Zhang, Junju; Chang, Bengkang; Sun, Bin; Li, Yingjie
• Is Part Of: IET image processing. Volume 8:Issue 7(2014)
• Page Start: 426
• Page End: 433
• Publication Date: 2014-07-01
• Subjects: image motion analysis -- image segmentation -- Markov processes -- maximum likelihood estimation -- unsupervised learning -- video signal processing; unsupervised evaluation method -- Markov random field -- moving object segmentation -- infrared videos -- performance evaluation -- edge features -- spatio‐temporal information -- MRF model -- maximum a posteriori estimation principle -- subjective evaluation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2013.0356
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : An unsupervised method is proposed for performance evaluation of the moving object segmentation using Markov random field (MRF) in infrared videos. This method focuses on the edge features and takes spatio‐temporal information into account. The authors consider an MRF model for each edge point of a segmentation mask in spatial and temporal directions. This problem is then formulated using maximum a posteriori estimation principle to form a criterion of evaluation. Subjective evaluation is applied to measure the performance of the evaluation methods. The results show that the proposed method is superior to other unsupervised measures.