Multimedia applications deal, in most cases, with an extremely high volume of multimedia data (2D and 3D images, sounds, videos). That is why efficient algorithms should be developed to analyze and process these large datasets. On the other hand, multimedia management is based on efficient representation of knowledge which allows efficient data processing and retrieval. The main challenge in this era is to achieve clever and quick access to these huge datasets to allow easy access to the data

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... d in a reasonable time. In this context, large-scale image retrieval is a fundamental task. Many methods have been developed in the literature to achieve fast and efficient navigating in large databases by using the famous content-based image retrieval (CBIR) methods associated with these methods allowing a decrease in the computing time, such as dimensional reduction and hashing methods. More recently, these methods based on convolutional neural networks (CNNs) for feature extraction and image classification are widely used. In this paper, we present a comprehensive review of recent multimedia retrieval methods and algorithms applied to large datasets of 2D/3D images and videos. This editorial paper discusses the mains challenges of multimedia retrieval in a context of large databases.