Paper Info
Title
Image features extractor based on hybridization of fuzzy controller and meta-heuristic
Abstract
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.
Year
DOI
Venue
2021
10.1109/FUZZ45933.2021.9494580
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
DocType
ISSN
fuzzy logic,meta-heuristic,local/global features,image processing,image classification
Conference
1544-5615
ISBN
Citations 
PageRank 
978-1-6654-4408-8
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Dawid Polap118628.52
Marcin Wozniak23613.22