Cosine metric supervised deep hashing with balanced similarity

Deep supervised hashing takes prominent advantages of low storage cost, high computational efficiency and good retrieval performance, which draws attention in the field of large-scale image retrieval. However, similarity-preserving, quantization errors and imbalanced data are still great challenges in deep supervised hashing. This paper proposes a pairwise similarity-preserving deep hashing scheme to handle the aforementioned problems in a unified framework, termed as Cosine Metric Supervised Deep Hashing with Balanced Similarity (BCMDH). BCMDH integrates contrastive cosine similarity and Cosine distance entropy quantization to preserve the original semantic distribution and reduce the quantization errors simultaneously. Furthermore, a weighted similarity measure with cosine metric entropy is designed to reduce the impact of imbalanced data, which adaptively assigns weights according to sample attributes (pos/neg and easy/hard) in the embedding process of similarity-preserving. The experimental results on four widely-used datasets demonstrate that the proposed method is capable of generating hash codes of high quality and improve largescale image retrieval performance.