Feature Based Fuzzy Rule Base Design for Image Extraction

TL;DR

This paper introduces a feature-based fuzzy rule system for image extraction that automates image segmentation without external intervention, showing improved accuracy over existing methods using metrics like MSE, MAE, and PSNR.

Contribution

A novel fuzzy rule-based technique for feature-driven image segmentation that operates autonomously and outperforms traditional methods in accuracy.

Findings

The proposed method achieves lower MSE, MAE, and higher PSNR compared to existing techniques.

Experimental results validate the efficiency and effectiveness of the fuzzy rule-based approach.

The method is applicable across various domains such as medical imaging, industrial inspection, and robotics.

Abstract

In the recent advancement of multimedia technologies, it becomes a major concern of detecting visual attention regions in the field of image processing. The popularity of the terminal devices in a heterogeneous environment of the multimedia technology gives us enough scope for the betterment of image visualization. Although there exist numerous methods, feature based image extraction becomes a popular one in the field of image processing. The objective of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation and subsequent extraction can be considered the first step and key issue in object recognition, scene understanding and image analysis. Its application area encompasses mobile devices, industrial quality control, medical appliances, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the preprocessing work. Most of the times, acquiring spurious-free preprocessing data requires a lot of application cum mathematical intensive background works. We propose a feature based fuzzy rule guided novel technique that is functionally devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR).