Deep Hashing With Multi-Central Ranking Loss for Multi-Label Image Retrieval

With the explosive growth of various images, large-scale image retrieval has attracted ever-growing attention. Deep hashing methods have achieved great success on single-label retrieval. However, the multi-level similarities between samples in multi-label scenarios have not been fully explored. In this letter, based on the in-depth analysis of the complex semantic similarities of multi-label images, the Multi-Central Ranking Loss (MCR Loss) for deep hashing is proposed to construct a powerful metric space that not only preserves the fine-grained similarities of multi-label images but also has low quantization error. The proposed MCR Loss utilizes learnable hash centers and similarities of data-to-data pairs to optimize the metric space, which greatly alleviates the embedding conflict caused by proxy-based supervision, and reduces the quantization error. The proposed method is compared with several existing state-of-the-art hashing methods on two public multi-label benchmarks. Experimental results show that the proposed method achieves state-of-the-art performance on several ranking evaluation metrics.