  In recent years, deep-networks-based hashing has become a leading approach
for large-scale image retrieval. Most deep hashing approaches use the high
layer to extract the powerful semantic representations. However, these methods
have limited ability for fine-grained image retrieval because the semantic
features extracted from the high layer are difficult in capturing the subtle
differences. To this end, we propose a novel two-pyramid hashing architecture
to learn both the semantic information and the subtle appearance details for
fine-grained image search. Inspired by the feature pyramids of convolutional
neural network, a vertical pyramid is proposed to capture the high-layer
features and a horizontal pyramid combines multiple low-layer features with
structural information to capture the subtle differences. To fuse the low-level
features, a novel combination strategy, called consensus fusion, is proposed to
capture all subtle information from several low-layers for finer retrieval.
Extensive evaluation on two fine-grained datasets CUB-200-2011 and Stanford
Dogs demonstrate that the proposed method achieves significant performance
compared with the state-of-art baselines.
