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In recent years, unsupervised deep hashing methods have achieved great success in large-scale image retrieval. However, these approaches still suffer two major problems in real world applications. On the one hand, due to the lack of effective supervision information, hash codes of different categories are easily judged to be similar. On the other hand, binary semantic similarity matrices can not reflect ranking relationship and the internal structure information of different images. To solve these problems, we propose a novel unsupervised deep hashing method, named <bold>P</bold>seudo labels and <bold>S</bold>oft multi-part <bold>C</bold>orresponding similarity based <bold>H</bold>ashing (PSCH), to ensure the heterogeneity of the hash codes. Specifically, we propose a &#x201C;pseudo labels&#x201D; method that use <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-means clustering and a distance threshold to generate the pseudo labels. Further, in order to reflect the hash codes similarity between instances within the same class, we propose a novel soft multi-part corresponding similarity method to learn better hash codes. This method can divide deep feature maps into several groups and compute the attention map for multi-part similarity matrices. In addition, a novel loss function is proposed to support learning with pseudo labels and soft multi-part corresponding similarity for achieving better performance. Comprehensive experiments on CIFAR-10, NUSWIDE, and Flickr demonstrate that our method can generate high-quality hash codes and outperform state-of-the-art unsupervised hashing methods by a large margin.

Pseudo Labels and Soft Multi-Part Corresponding Similarity for Unsupervised Deep Hashing