With the rapid development of deep learning methods, researchers have gradually shifted
the research focus from hand-crafted features to deep features in the field of the content …

Content-based image retrieval (CBIR) consists in searching the most similar images to the
query content from a given pool of images or database. Existing works' success relies on …

Learning effective feature representations and similarity measures are crucial to the retrieval
performance of a content-based image retrieval (CBIR) system. Despite extensive research …

Deep learning based feature extraction combined with visual attention mechanism is shown
to provide good results in content-based image retrieval (CBIR). Ideally, CBIR should rely on …

Research on content-based image retrieval (CBIR) has been under development for
decades, and numerous methods have been competing to extract the most discriminative …

A content-based image retrieval (CBIR) system works on the low-level visual features of a
user input query image, which makes it difficult for the users to formulate the query and also …

This paper presents a new content-based image retrieval (CBIR) method based on image
feature fusion. The deep features are extracted from object-centric and place-centric deep …

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 …

Convolutional neural networks (CNNs) with deep learning have recently achieved a
remarkable success with a superior performance in computer vision applications. Most of …

Multimedia content analysis is applied in different real‐world computer vision applications,
and digital images constitute a major part of multimedia data. In last few years, the …