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. 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. 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. 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.