局部不变特征是近年来计算机视觉领域的研究热点。局部不变特征在宽基线匹配、特定目标识别、目标类别识别、图像及视频检索、机器人导航、场景分类、纹理识别和数据挖掘等多个领域得到了广泛的应用。本文基于局部不变特征检测、局部不变特征描述和局部不变特征匹配3个基本问题，综述了文献中现有的局部不变特征研究方法，并比较了各类方法的优缺点。根据特征层次的不同，局部不变特征检测方法可以分为角点不变特征、blob不变特征和区域不变特征检测方法3类。局部不变特征的描述方法可以分为基于分布的描述方法、基于滤波的描述方法、基于矩的描述方法和其他描述方法。局部不变特征匹配的研究主要集中在相似性度量、匹配策略和匹配验证3个方面。最后在分析各类研究方法的基础上，总结了局部不变特征研究目前存在的一些问题及可能的发展方向。

Local invariant features are receiving increasing attention from computer vision research community. Local invariant features have been widely utilized in a large number of applications, e.g., wide baseline matching, object recognition, and categorization, image retrieval, visual search, robot localization, scene classification, texture recognition and data mining. This paper gives an overview of the various approaches and properties of local invariant features. We focus on three major areas: (1) local invariant feature detectors, (2) local invariant feature descriptors, and (3) local invariant feature matching. Most of the existing local invariant feature detectors can be categorized into corner detectors, blob detectors or region detectors. Local descriptors can be categorized into distribution-based, filter-based, moment-based descriptors and others descriptors. Similarity measurement, matching strategy and matching verification are three key components of robust matching algorithms. Finally, some research challenges and future directions are discussed.