Local Semantic-aware Deep Hashing with Hamming-isometric Quantization.

Hashing has attracted increasing attention due to its tremendous potential for efficient image retrieval and data storage. Compared with conventional hashing methods with a handcrafted feature, emerging deep hashing approaches employ deep neural networks to learn feature representations as well as hash functions, which have already been proved to be more powerful and robust in real-world applications. Currently, most of the existing deep hashing methods construct pairwise or triplet-wise constraint to obtain similar binary codes between similar data pair or relative similar binary codes within a triplet. However, some critical local structures of the data are lack of exploiting, thus the effectiveness of hash learning is not fully shown. To address this limitation, we propose a novel deep hashing method named local semantic-aware deep hashing with Hamming-isometric quantization (LSDH), where local similarity of the data is intentionally integrated into hash learning. Specifically, in the Hamming space, we exploit the potential semantic relation of the data to robustly preserve their local similarity. In addition to reducing the error introduced by binary quantizing, we further develop a Hamming-isometric objective to maximize the consistency of similarity between the pairwise binary-like feature and its binary codes pair, which is shown to be able to enhance the quality of binary codes. Extensive experimental results on several benchmark datasets, including three singlelabel datasets (i.e., CIFAR-10, CIFAR-20, and SUN397) and one multi-label dataset (NUS-WIDE), demonstrate that the proposed LSDH achieves superior performance over the latest state-of-theart hashing methods.