Robust deep supervised hashing for image retrieval

Abstract

Hashing is an important technique branch of image retrieval due to its satisfactory retrieval performance with high retrieval speed and low storage cost. Deep supervised hashing methods, which take advantage of the convolutional neural network and the supervised information, have shown better performance than other kinds of hashing methods. However, previous deep hashing methods do not consider the noisy data, which generally exist in large-scale labeled datasets and mislead the learning algorithm. In this paper, we propose a novel robust deep supervised hashing (RDSH) method, in which a robust pairwise loss and a quantitation loss are used to supervise the learning procedure. The quantitation loss guides the CNN to output binary codes. The robust pairwise loss for similarity-preserving learning is designed based on generalizations of the exponential and logarithmic functions. By adjusting its parameters, the robust pairwise loss can exhibit special properties, including tail-heaviness and boundedness, which results in the robustness of the learning procedure to noisy training data. To verify the robustness of the RDSH, we conduct experiments on CIFAR10 with different noisy levels, in which RDSH shows better robustness than other deep supervised hashing methods. Experiments on standard CIFAR-10 and NUS-WIDE datasets show that RDSH outperforms other baselines.