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Multi-features association-based local HOG description for image matching

Image matching has always been a very important research areas in computer vision. The performance will directly affect the matching results. Among local descriptors, the Scale Invariant Feature Transform(SIFT) is a milestone in image matching, while HOG as an excellent descriptor is widely used in 2D object detection, but it seldom used as a descriptor for matching. In this article, we suppose to pool these algorithms and we use a simple modification of the Rotation- Invariant HOG(RI-HOG) to describe the feature domain detected by SIFT. The RI-HOG is Fourier analyzed in the polar/spherical coordinates. Later in our experiment, we test the performance of our method on a datasets. We are surprised to find that the method outperforms other descriptors in image matching in accuracy.