Piecewise Hashing: A Deep Hashing Method for Large-Scale Fine-Grained Search

Fine-grained search task such as retrieving subordinate categories of birds, dogs or cars, has been an important but challenging problem in computer vision. Although many effective fine-grained search methods were developed, with the amount of data increasing, previous methods fail to handle the explosive fine-grained data with low storage cost and fast query speed. On the other side, since hashing sheds its light in large-scale image search for dramatically reducing the storage cost and achieving a constant or sub-linear time complexity, we leverage the power of hashing techniques to tackle this valuable yet challenging vision task, termed as fine-grained hashing in this paper. Specifically, our proposed method consists of two crucial modules, i.e., the bilinear feature learning and the binary hash code learning. While the former encodes both local and global discriminative information of a fine-grained image, the latter drives the whole network to learn the final binary hash code to present that fine-grained image. Furthermore, we also introduce a novel multi-task hash training strategy, which can learn hash codes of different lengths simultaneously. It not only accelerates training procedures, but also significantly improves the fine-grained search accuracy. By conducting comprehensive experiments on diverse fine-grained datasets, we validate that the proposed method achieves superior performance over the competing baselines.