THEORETICAL FOUNDATIONS AND EFFICIENT ALGORITHMS FOR FEATURE DETECTION AND EXTRACTION (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
Feature detection and extraction play a critical role in numerous applications within artificial intelligence, computer vision, and data science. As datasets grow in complexity and volume, traditional feature extraction techniques often struggle to maintain efficiency and accuracy. This paper explores the theoretical foundations and efficient algorithms for feature detection and extraction, emphasizing the intersection of classical statistical methods and modern machine learning-based approaches. The study provides an in-depth analysis of key methodologies, including edge detection algorithms, deep learning-based feature extraction, and dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, the research highlights the challenges associated with computational efficiency, accuracy trade-offs, and real-time processing constraints. A critical gap in existing literature is identified—the disconnect between theoretical models and their practical implementations—which often hinders the optimization of feature extraction processes. By synthesizing current advancements and addressing these challenges, this paper aims to provide a comprehensive framework for improving feature detection and extraction methodologies, ultimately contributing to the development of more efficient and adaptable systems across various domains.
Keywords: Feature detection; feature extraction; machine learning; deep learning; dimensionality reduction; computer vision; artificial intelligence; edge detection; real-time processing; Principal Component Analysis (PCA); t-Distributed Stochastic Neighbor Embedding (t-SNE)