Texture matching with best buddies similarity

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and basic method used to obtain similarities between objects using certain mathematical algorithms. In this study, template matching is introduced, template matching approaches and some important template matching techniques are explained and mathematical structures are represented. A wide range of template matching application areas have been reviewed and template matching applications have been implemented in different areas for object recognition. In particular, a new template matching method known as Best Buddies Similarity (BBS) is described, its properties are analyzed, and its implementation has been done in some areas. The performance of the BBS, which was successful in sequential frames, in different template matching application areas was evaluated. Also, some template matching methods commonly used in the literature for template matching (Square Difference Matching Method, Correlation matching methods, Correlation Coefficients Matching Methods and Normalized methods) are also described along with their mathematical formulas. With these methods, applications have been realized in areas such as texture matching and leaf recognition. In the common practices realized, the performance of these methods with BBS is compared. Keywords: Template Matching, Image Matching, Image Recognition, Texture Matching, Texture Recognition, Leaf Recognition, Leaf Classification