Hierarchical fuzzy deep learning for image classification

Abstract

Considerable interest has been shown over the last several decades for fuzzy logic and its application. The intelligent and deep learning systems are gaining breakthroughs in all walks of life to solve real-life problems for the future. The conventional fuzzy has the constraint to work with limited rule dimensions, whereas deep neural networks are unable to handle uncertain and imprecise data implicitly in the system. The objective of this paper is to develop a generalized algorithm for intelligent systems that can handle uncertainty and imprecise behavior especially for processing of large image datasets. In this paper, the hierarchical fuzzy approach is suggested, as it is gaining attention to tackle large real-life problems. The strategy used is to partition a large image dataset into small data samples and connect all the fuzzy subsystems in a hierarchical manner. In the literature, as far as authors know, no one has developed a hierarchical fuzzy approach to handle a large image dataset of real images. The algorithm for hierarchical fuzzy logic for a large image data using image thresholding has been discussed. To make the assessment, the real image database has been considered. The image classification has attained the potential applications to defense and security especially for target identification and classification. The accuracy and computational time comparisons of hierarchical fuzzy systems with existing methodologies such as deep neural networks have been discussed.

Keywords

• Hierarchical deep learning
• Hierarchical fuzzy
• Hierarchical systems
• Image classification and identification
• Target acquisition
• Target detection