Contourlet-SIFT Feature Matching Algorithm

The Scale Invariant Feature Transform (SIFT) has a fine algorithm performance and an extensive application to the matching algorithm of local features, but its descriptor is characterized by a high dimension and huge time consumption also gives rise to a low matching robustness when tackling similar areas. Therefore this paper puts forward an innovative Contourlet-SIFT feature matching algorithm. The SIFT key points are first extracted to conduct Contourlet transformation on peripheral areas in order to calculate the mean and standard deviation of the decomposition coefficient in each direction. Then the vector of overall texture description is constructed and the Euclidean distance of this low-dimensional vector provides references for prioritizing the matching pairs. The first 1% key points will be subject to the nearest ratio matching by the SIFT vector. The result proves that the new algorithm surpasses SIFT especially when addressing the images with great brightness difference and many similar areas. It can lift the matching speed while it parallels SIFT in its invariability of scale, rotation and visual angle.