Parameters Optimization Method of the Information-Extreme Object Recognition System on the Terrain

摘要

Proposed method for the synthesis of information extreme classifier of images with rough binary encoding of sparse histogram of frequency of occurrence of visual words, to provide a computationally efficient decision rules by small volume dataset with reliability which approaches boundary maximum value Improved method based on population search to adjust parameters of the extractor features that allows you to get the best value in the information and cost meaning of the parameters functioning system recognition of images in a few iterations of the algorithm work. This experiment shows the advantage of the use swarm algorithm for scanning images, which is three-fold increase in performance compared to known algorithms RASW and ESS. The practical value of the results is to obtain well-functioning designing algorithms capable of learning image recognition, which operates under conditions of resource limitations and information.