Abstract: Recently,deep hashing technology,with its advantages of high storage efficiency and quick query speed,has been widely investigated in the field of large-scale visual image retrieval.However,the fundamental image features obtained by current deep hashing methods mainly depend on the first-order statistics of deep convolutional features,and they seldom take the global structure into consideration,leading to the limitation of retrieval accuracy to a certain degree.Focusing on the global representation capability and the similarity of intra-class samples,this paper proposes a novel supervised similarity preserving deep second-order hashing (S2PDSoH) based on deep pairwise supervised hashing,gaining effective performance improvement in the image retrieval task.Based on the pair-wise deep hashing model,S2PDSoH first employs covariance estimation based on matrix power normalization method to capture the deep second-order information of sample images,so that hash codes can possess good global presentation ability.Then,to gain more robust hash codes,it further constructs a joint constraint of category supervision and similarity preservation motivated by the idea of multi-loss integration,followed by an alternate iteration optimization algorithm to realize the end-to-end training.Therefore,with the semantic information added to the dual channel deep second-order hashing framework,S2PDSoH establishes a mechanism for common constraints on category supervision and similarity maintenance.In addition,it also introduces a hash-like function to achieve the approximate binarization result of hash codes,which solves the problem of non-convexity and avoids the quantization error in the hash mapping process.Extensive experimental results on three commonly used data sets show the effectiveness of the proposed deep-order hashing method with supervised similarity preservation.

Key words: Deep hashing, Second-order statistic modeling, Category supervision, Similarity preserving, Image retrieval