Enhancing Image Retrieval Systems: A Comprehensive Review of Machine Learning Integration In CBIR
Keywords:
Content-Based Image Retrieval (CBIR), Machine Learning in CBIR, Image Feature Extraction, Global Image Features, Local Image Features, Semantic Gap in Image Retrieval, Visual Vocabulary, SIFT, HOG.Abstract
This paper presents a comprehensive review of advancements and methodologies in Content-Based Image Retrieval (CBIR) systems, with a focus on integrating machine learning algorithms to enhance performance. It examines the CBIR framework, covering key stages from feature extraction to similarity measurement and image retrieval, and underscores the importance of both global (color, texture, shape) and local (keypoints, patterns) features in image representation. The paper explores various extraction methods and their effects on retrieval accuracy, categorizing features into global and local, and discussing their roles and limitations. The application of machine learning in CBIR is divided into unsupervised learning (clustering), supervised learning (classification), and deep learning. It evaluates algorithms like K-means, SVM, ANN, and CNN in the context of CBIR, analyzing recent literature to assess their functionality and challenges. Deep learning, especially CNNs, is highlighted as a promising approach due to its strengths in translation, scale, rotation invariance, and direct learning from data. The paper identifies research gaps, including issues related to effective feature fusion, the development of scalable methods for large databases, and the integration of machine learning for better semantic understanding. It concludes by emphasizing the importance of addressing these gaps to improve CBIR systems in terms of retrieval performance, scalability, and efficiency. This review provides valuable insights for researchers and practitioners, offering a detailed overview of current trends and future directions in CBIR.
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