Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Abstract

In the era of massive data production through the internet and social media, the volume of images generated is immense. Storing and retrieving relevant images efficiently pose significant challenges. Content-based image retrieval (CBIR) has emerged as a prevalent method for retrieving relevant images based on query images from large image collections. CBIR relies on three fundamental elements: the selection, extraction, and representation of features. This paper delves into a comprehensive survey of these crucial aspects. This paper begins by investigating the significance and wide-ranging applications of CBIR. It subsequently delves into an intricate analysis of feature selection, encompassing attributes such as color, texture, shape, and descriptors. Following this, the paper navigates through sections dedicated to feature extraction techniques and their subsequent representation. Furthermore, this paper includes an assessment of recent research articles and the methodologies they employ within the realm of CBIR. Significantly, CBIR has witnessed a notable expansion to incorporate deep learning techniques in recent times. The survey presents an overview of these recent methods and their integration into CBIR frameworks. This paper concludes by offering an extensive outline of 215 articles, encompassing a wide range of analyses conducted within the field of CBIR. Finally, this paper also outlines potential research directions for the future. It sheds light on areas where CBIR can continue to evolve and enhance its capabilities.

Keywords

• Content-based image retrieval
• text-based image retrieval
• deep learning
• image processing
• image retrieval
• color feature