Open AccessUnsupervised evaluation method using Markov random field for moving object segmentation in infrared videos
Author(s) -
Min Chaobo,
Zhang Junju,
Chang Bengkang,
Sun Bin,
Li Yingjie
Publication year - 2014
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0356
Subject(s) - markov random field , segmentation , artificial intelligence , computer science , pattern recognition (psychology) , maximum a posteriori estimation , image segmentation , a priori and a posteriori , computer vision , enhanced data rates for gsm evolution , markov process , hidden markov model , point (geometry) , object (grammar) , markov chain , scale space segmentation , mathematics , machine learning , maximum likelihood , statistics , philosophy , geometry , epistemology
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.