Local multi-feature hashing based fast matching for aerial images

Despite their superior accuracy, existing floating-point feature descriptor based image matching algorithms are too computationally intensive for real world aerial imaging applications, where target images are often of extremely high resolutions. To improve the efficiency of these algorithms, this paper presents a novel low-complexity image matching approach, namely local multi-feature hashing (LMFH). Similar to conventional techniques, LMFH employs a floating-point local multi-feature descriptor for accurate matching, however, the proposed method does not compare descriptors directly. Instead, LMFH projects each high-dimensional descriptor to a compact binary code in Hamming space using a trained hash function and then performs the feature comparison in the mapped domain. Experimental results show that, in terms of the computational cost, LMFH is on pair with the most efficient modern binary descriptors. Furthermore, LMFH has a performance gain which is significantly higher than the state-of-the-art binary descriptors and is comparable with conventional floating-point descriptors in terms of accuracy.