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Semantic content-based image retrieval: A comprehensive study

机译：基于语义内容的图像检索：综合研究

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摘要

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts. (C) 2015 Elsevier Inc. All rights reserved.
机译：在当前的数字世界中，多媒体内容的复杂性正在显着增加。这就迫切需要开发高效的检索系统来满足人类的需求。最近，在基于内容的图像检索（CBIR）领域已经提出并进行了广泛的研究。这些努力的大部分集中在缩小由数字机器代表的低级图像特征与用于感知图像的高级人类感知之间存在的语义鸿沟。基于近年来不断增长的研究，本文对CBIR领域的最新技术进行了全面回顾。此外，本研究还详细介绍了CBIR框架和已实现的改进。包括图像预处理，特征提取和索引编制，系统学习，基准数据集，相似性匹配，相关性反馈，性能评估和可视化。最后，提供了有前途的研究趋势，挑战和我们的见识，以激发进一步的研究工作。 （C）2015 Elsevier Inc.保留所有权利。

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