A comprehensive review of current local features for computer vision

By Jing Li, Nigel M. Allinson. Neurocomputing, 2008

318 citations recorded by the source corpus.

Local features are widely utilized in a large number of applications, e.g., object categorization, image retrieval, robust matching, and robot localization. In this review, we focus on detectors and local descriptors. Both earlier corner detectors, e.g., Harris corner detector, and later region detectors, e.g., Harris affine region detector, are described in brief. Most kinds of descriptors are described and summarized in a comprehensive way. Five types of descriptors are included, which are filter-based descriptors, distribution-based descriptors, textons, derivative-based descriptors and others. Finally, the matching methods and different applications with respect to the local features are also mentioned. The objective of this review is to provide a brief introduction for new researchers to the local feature research field, so that they can follow an appropriate methodology according to their specific requirements.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/77b075cdcf9de8f2b26a831ad7935288e04498e1

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images (2011, methodology): “Feature correspondence is based on either spatial relationships [15], [16] or feature descriptor similarities [14], [17].”
A survey on vision-based UAV navigation (2018, background): “For the last three decades, a comprehensive study of feature detection and description has been undertaken and there have been various types of feature detectors and descriptors (Li and Allinson 2008; Tuytelaars and Mikolajczyk 2008).”
Distance based similarity models for content based multimedia retrieval (2013, methodology): “After having identified interesting features within an image, they are described mathematically by feature descriptors [Penatti et al., 2012, Li and Allinson, 2008, Deselaers et al., 2008, Mikolajczyk and Schmid, 2005].”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, background): “The reader is referred to several core texts about features for more information [50–53].”