Abstract-In this paper, an entropy-functional-based online adaptive decision fusion (EADF) 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 subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. 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 arrive 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-Active learning, decision fusion, entropy maximization, online learning, projections onto convex sets, wildfire detection using video.
al. REF propose an entropy-functional-based online adaptive decision fusion (EADF) framework for image analysis and computer vision applications.
Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Medicine Computer Science