Deep Piecewise Hashing for Efficient Hamming Space Retrieval

Hamming space retrieval can achieve constant-time image search, which is more efficient than linear scan. In Hamming space retrieval, the data points inside the Hamming ball imply retrievable while the data points outside are irretrievable. Therefore, it is crucial to explicitly characterize the Hamming ball. However, for the existing Hamming space retrieval methods, many similar points are found close to the outside of the Hamming ball while many dissimilar points are found close to the query point, leading to the decline of both retrieval accuracy and recall. In this paper, we present a novel method named Deep Piecewise Hashing (DPH), for Efficient Hamming Space Retrieval. A piecewise loss is elaborately designed to guide the learning of hash codes. Meanwhile, a piecewise probability distribution is introduced in the proposed loss function. The piecewise probability distribution pays more attention to the learning of those "marginal" similar points. It considers both discrimination and robustness for the dissimilar points inside the Hamming ball. Comprehensive experiments on two datasets, MS-COCO and NUS-WIDE, demonstrate that DPH can yield state-of-the-art Hamming space retrieval performance.