Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains.

翻译：基于内容的图像检索(CBIR)系统已成为计算机视觉领域的关键工具，支持基于视觉内容而非仅依赖元数据进行图像搜索。本综述全面概述了CBIR技术，重点阐述其在目标检测中的作用及其基于内容特征识别与检索视觉相似图像的潜力。文中探讨了CBIR系统面临的语义鸿沟、可扩展性等挑战及其潜在解决方案，深入阐释了由低层特征与高层语义概念差异引发的语义鸿沟问题，并探索了弥合这一鸿沟的方法。其中一个重要解决方案是集成相关反馈(RF)技术——该技术允许用户对检索结果提供反馈，从而迭代优化搜索结果。本文综述了利用RF提升CBIR精度与相关性的长期与短期学习方法，这些方法聚焦于权重优化，并采用主动学习算法选择训练分类器的样本。此外，本文还研究了机器学习技术以及深度学习和卷积神经网络在提升CBIR性能中的应用。本综述在促进对CBIR与RF技术认知方面发挥重要作用，为研究人员和实践者理解现有方法、挑战及潜在解决方案提供指导，同时推动知识传播并识别研究空白。通过展望未来研究方向，本文为CBIR技术进展奠定基础，旨在提升其在多应用领域的检索准确性、可用性与有效性。