Abstract
Classical local descriptors refer to those were proposed many years ago but have a profound influence on the development of local image description as well as related applications. Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are the two widely used descriptors in computer vision. Especially for SIFT, it is an extremely popular solution to various applications, ranging from object recognition, image retrieval, to structure from motion, etc. While for SURF, it is a first and predominant choice for those applications requiring fast or near real-time image matching until the very recent flourish of binary descriptors. Another classical local feature is Local Binary Pattern (LBP) proposed in the 1990s. Along with many variants, LBP has been ubiquitous in texture classification and many face-related tasks, e.g., face recognition, face detection, and facial expression recognition. Because of their popularity, we choose to introduce them in detail in this chapter.
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Fan, B., Wang, Z., Wu, F. (2015). Classical Local Descriptors. In: Local Image Descriptor: Modern Approaches. SpringerBriefs in Computer Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49173-7_2
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DOI: https://doi.org/10.1007/978-3-662-49173-7_2
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