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The Scientific World Journal
Volume 2013, Article ID 572393, 10 pages
Research Article

Sample Training Based Wildfire Segmentation by 2D Histogram -Division with Minimum Error

1School of Computer, Wuhan University, Wuhan, Hubei 430072, China
2Suzhou Institute of Wuhan University, Suzhou, Jiangsu 215123, China
3Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15213, USA

Received 15 March 2013; Accepted 3 June 2013

Academic Editors: I. Korpeoglu, E. Pauwels, and S. Verstockt

Copyright © 2013 Jianhui Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram -division and minimum error. Based on minimum error principle and 2D color histogram, the -division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually labeled fire pixels. Then we define the probability function of error division to evaluate -division segmentations, and the optimal angle is determined by sample training. Performances in different color channels are compared, and the suitable channel is selected. To further improve the accuracy, the combination approach is presented with both -division and other segmentation methods such as GMM. Our approach is tested on real images, and the experiments prove its efficiency for wildfire segmentation.