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.