Image features extractor based on hybridization of fuzzy controller and meta-heuristic

The image recognition task is one of the fundamental aspects of image and video analysis. Recognition of individual objects allows for further inference or analysis. Unfortunately, quite often the detection and recognition itself are difficult tasks. Especially if there are many different objects in the image, or if there is some noise. In this paper, we propose a method for extracting specific features from images. The proposition is a hybridization of two main tools - meta-heuristic and fuzzy system. At first, an objective function is created for a specific object, then the meta-heuristic is used for analyzing an image for finding the best features. The operation of creating an objective function and then interpreting the position of individuals in the metaheuristic is evaluated by a fuzzy controller. The use of fuzzy logic enables the creation of decision sets during data analysis. This is possible through the adaptive technique of improving the value of the membership functions in Takagi-Sugeno systems. A fuzzy approach shows great potential in analyzing the position in the image. The proposed feature extraction mechanism has been tested and discussed due to the possibility of using fuzzy logic as well as its hybridization with meta-heuristics.