A ranking hashing algorithm based on listwise supervision - Anbang YANG; Jiangbo QIAN; Yihong DONG; Huahui CHEN (Dianxin kexue)
Recently,learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present,most ranking hashing algorithms compare the consistency of data in the Euclidean space and the Hamming space to construct the loss function.However,because the Hamming distance is a discrete integer value,there may be many data points sharing the same Hamming distance result in the exact ranking cannot be performed.To address this challenging issue,the encoded data was divided into several subspaces with the same length.Each subspace was set with different weights.The Hamming distance was calculated according to different subspace weights.The experimental results show that this algorithm can effectively sort the data in the Hamming space and improve the accuracy of the query compared with other learning to hash algorithms.