Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video

O. Gunay, B. U. Toreyin, K. Kose, A. E. Cetin
2012 IEEE Transactions on Image Processing  
In this paper, an entropy functional based online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion
more » ... thod based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrives sequentially and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented. Index Terms-Projections onto convex sets, active learning, decision fusion, online learning, entropy maximization, wildfire detection using video.
doi:10.1109/tip.2012.2183141 pmid:22249709 fatcat:2apizyvqbjeahkzchxivoxml24