The similarity analysis is a major issue in computer vision. This concept is denoted by a scalar which designates a distance measure giving the resemblance of two objects. Specifically, this distance is used in many areas such as image compression, image matching, biometrics, shape recognition, objects recognition, manufacturing industry, data analysis, etc. Several studies have shown that the choice of similarity measures depends on the type of data. This paper presents an evaluation of some similarity measures in the literature and a proposed similarity function taking into account image feature. The features concerned are textures and key-points. The data used in this study came from multispectral imaging by using visible and thermal infrared images.
Mamadou Diarra, Pierre Gouton, Adou Kablan Jérôme, "Assessing the useful of similarity measures for multispectral face recognition" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXIII: Displaying, Processing, Hardcopy, and Applications, 2018, pp 361-1 - 361-6, https://doi.org/10.2352/ISSN.2470-1173.2018.16.COLOR-361