  Retrieving content relevant images from a large-scale fine-grained dataset
could suffer from intolerably slow query speed and highly redundant storage
cost, due to high-dimensional real-valued embeddings which aim to distinguish
subtle visual differences of fine-grained objects. In this paper, we study the
novel fine-grained hashing topic to generate compact binary codes for
fine-grained images, leveraging the search and storage efficiency of hash
learning to alleviate the aforementioned problems. Specifically, we propose a
unified end-to-end trainable network, termed as ExchNet. Based on attention
mechanisms and proposed attention constraints, it can firstly obtain both local
and global features to represent object parts and whole fine-grained objects,
respectively. Furthermore, to ensure the discriminative ability and semantic
meaning's consistency of these part-level features across images, we design a
local feature alignment approach by performing a feature exchanging operation.
Later, an alternative learning algorithm is employed to optimize the whole
ExchNet and then generate the final binary hash codes. Validated by extensive
experiments, our proposal consistently outperforms state-of-the-art generic
hashing methods on five fine-grained datasets, which shows our effectiveness.
Moreover, compared with other approximate nearest neighbor methods, ExchNet
achieves the best speed-up and storage reduction, revealing its efficiency and
practicality.
