目的 局部图像描述符广泛应用于许多图像理解和计算机视觉应用领域,如图像分类、目标识别、图像检索、机器人导航、纹理分类等。SIFT算法的提出标志着现代局部图像描述符研究的开始。主要对最近发展的现代局部图像描述符进行了综述。方法 首先,介绍了4大类局部图像描述符:局部特征空间分布描述符、局部特征空间关联描述符、基于机器学习的局部描述符、扩展局部描述符(局部颜色描述符、局部RGB-D描述符、局部空时描述符)。对局部图像描述符进行了分析和分类,并总结了局部图像描述符的不变性、计算复杂度、应用领域、评价方法和评价数据集。最后,展望了局部图像描述符的未来研究方向。结果 近年来局部图像描述符研究取得了很大进展,提出了很多优秀的描述符,在辨别性、鲁棒性和实时性方面有了很大提高,应用领域不断拓展。结论 局部图像描述符应用广泛,是计算机视觉领域的重要基础研究。而目前,局部图像描述符还存在许多问题,还需进一步的深入研究。

Objective Local image descriptors are widely utilized in many image understanding and computer vision applications, such as image classification, object recognition, image retrieval, robot navigation, and texture classification. The development of the SIFT algorithm highlighted the beginning of modern local image descriptor research. Recently developed modern local image descriptors are surveyed in this study. Method Four types of local image descriptors, namely, spatial distribution descriptors of local features, spatial correlation descriptors of local features, local descriptors based on machine learning, and extended local descriptors (local color descriptors, local RGB-D descriptors, and local space-time descriptors), are presented. The local image descriptors are then analyzed and categorized. The performance of the local image descriptors is investigatedin terms of invariance, computation complexity, application field, evaluation methods, and evaluation datasets. Finally, this paper concludes with a discussion of directions for future research of the local image descriptors. Result In recent years, the research of local image descriptors has made great progress. Many excellent descriptors are proposed, and their performances have greatly improved in the distinctiveness, robustness and real-time, and their application fields are continually expanded. Conclusion Local image descriptors are widely utilized as an important and fundamental research field of computer vision. However, many problems in the use of these descriptors persist. This condition indicates that further research on local image descriptors is required.