IMPLEMENTATION OF FEATURE THEORY ON IMAGE PROCESSING PERFORMANCE IN PATTERN RECOGNITION: A COMPREHENSIVE REVIEW ANALYSIS

Abstract

The increasing need for precise and effective image-based pattern recognition systems, particularly in the age of artificial intelligence, is what spurred this work.  As the basis for classification and pattern interpretation, feature theory is essential to the extraction of visual details.  This study's main goal is to thoroughly examine the effects of applying different feature theories both conventional and deep learning-based on image processing efficiency in pattern recognition scenarios.  The study looks at pertinent literature using a qualitative method and thematic examination to find important themes about the resilience, efficiency, and efficacy of feature extraction algorithms. The results show that system performance is strongly impacted by image quality, with appropriate preprocessing increasing accuracy by as much as 20%.  Additionally, processing performance can be increased by up to 300% without sacrificing accuracy by combining dimensionality reduction approaches with hierarchical feature extraction algorithms.  Additionally, contemporary feature-based systems exhibit increased resistance to image noise and geometric fluctuations.  This study emphasizes the value of choosing feature extraction techniques based on needs and the possibility of combining conventional and contemporary paradigms to create adaptable and resource-efficient pattern recognition systems.