Hierarchical Fuzzy Deep Learning for Image Classification
Abstract
Considerable interest has been shown for the last several decades for fuzzy logic and its application. The intelligent systems and deep learning systems are gaining breakthroughs in all walks of life to solve the real-life problems for 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 main 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 large image dataset into small data samples and connect all the fuzzy subsystems in a hierarchical manner. To the best of authors knowledge, nobody has developed a hierarchical fuzzy approach to handle large image dataset of real images. The algorithm for hierarchical fuzzy logic for 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 the 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 07, 2020
- Accession Number
- AD1121729
Entities
People
- Harpreet Singh
- Shashank Kamthan
- Thomas Meitzler