A low-dimensional local descriptor incorporating TPS warping for image matching

This paper proposes a low-dimensional image descriptor combining shape characteristics and location information. The shape characteristics are obtained from simple rectangular patterns approximating the interest regions. Although the shape component of the descriptor does not perform as well as high-dimensional descriptors (e.g. SIFT) it can be built very quickly. Moreover, it usually provides enough correspondences to align locations of interest regions. The alignment is based on the thin plate spline (TPS) warping algorithm with control points automatically identified by our method. Subsequently, the aligned coordinates contribute additional dimensions to the descriptor. The process may be iterated several times until no further improvement is achieved. Experiments show that incorporation of location data into the descriptor improves performance. The proposed descriptor is compared to SIFT (a standard benchmark which is considered one of the best local descriptors [1]) for real images with various geometric and photometric transformations and for diversified types of scenes. Results show the proposed low-dimensional descriptor generally performs better than SIFT descriptor while the computational complexity of our descriptor is far superior.