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FSpH: Fitted spectral hashing for efficient similarity search
2014
Computer Vision and Image Understanding
Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among
doi:10.1016/j.cviu.2014.01.011
fatcat:qiktveqyvzcn3dpr4c7uw7kyda