Improved SURF in Color Difference Scale Space for Color Image Matching

Abstract

This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From the experiment results, we can know that, compared the other methods, the feature points detection method proposed is more robust. The matching scores and precision of our method are dominant among different methods of color image matching. Compared with SURF, the number of feature points detected by the proposed method increases by 163%, the average matching scores and matching precision increase by 16% and 15.81% respectively.