  Deep hashing models have been proposed as an efficient method for large-scale
similarity search. However, most existing deep hashing methods only utilize
fine-level labels for training while ignoring the natural semantic hierarchy
structure. This paper presents an effective method that preserves the classwise
similarity of full-level semantic hierarchy for large-scale image retrieval.
Experiments on two benchmark datasets show that our method helps improve the
fine-level retrieval performance. Moreover, with the help of the semantic
hierarchy, it can produce significantly better binary codes for hierarchical
retrieval, which indicates its potential of providing more user-desired
retrieval results.
