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Homography estimation is a fundamental problem in the field of computer vision. For estimating the homography between two images, one of the key issues is to match keypoints in the reference image to the keypoints in the moving image. To match keypoints in real time, a binary image descriptor, due to its low matching and storage costs, emerges as a more and more popular tool. Upon achieving the low costs, the binary descriptor sacrifices the discriminative power of using floating points. In this paper, we present BB-Homography, a new approach that fuses fast binary descriptor matching and bipartite graph for homography estimation. Starting with binary descriptor matching, BB-Homography uses bipartite graph matching (GM) algorithm to refine the matching results, which are finally passed over to estimate homography. On realizing the correlation between keypoint correspondence and homography estimation, BB-Homography iteratively performs the GM and the homography estimation such that they can refine each other at each iteration. In particular, based on spectral graph, a fast bipartite GM algorithm is developed for lowering the time cost of BB-Homography. BB-Homography is extensively evaluated on both public benchmarks and live-captured video streams that consistently shows that BB-Homography outperforms conventional methods for homography estimation. [ABSTRACT FROM PUBLISHER]