Signal and Data Processing

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The rapid increase in the volume, diversity, and complexity of visual content in the digital world has made the need for designing and implementing visual content search and retrieval systems highly evident. Currently, we are facing a massive scale of visual data on the web, for which the conventional approaches based on manual and human-generated metadata are not sufficient to handle the diversity and sheer volume. The enormous volume of data generated on the web, without a high-accuracy and high-speed solution for understanding and retrieving it, will join the digital archives forever and never be found again. Recently, there have been significant efforts for retrieving these images, particularly in the fields of Content-Based Image Retrieval (CBIR) and Semantic Image Retrieval (SIR). Content-based and semantic image retrieval systems have the capability to search and retrieve images based on their internal content and high-level human-understandable semantics, rather than just the metadata that may be associated with them. This paper provides a comprehensive review of the latest advancements in the field of content-based image retrieval in recent years. It aims to critically discuss the strengths and weaknesses of each research area in content-based retrieval, and provide an overall framework of this process and the progress made in areas such as image preprocessing, feature extraction and embedding, machine learning, benchmark datasets, similarity matching, and performance evaluation. Finally, the paper presents novel research approaches, challenges, and suggestions for better advancing research in this field.

Keywords: Content-Based Image Retrieval, Image Processing, Computer Vision, Machine Learning, Deep Learning, Semantic Gap

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