Deep Transfer Hashing for Image Retrieval

Hongjia Zhai, Shenqi Lai, Hanyang Jin, Xueming Qian, Tao Mei

Abstract: Deep supervised hashing has emerged as an influential solution to large-scale semantic image retrieval problems in computer vision. In the light of recent progress, image label is the common way to define whether two images belong to the same category, but it contains little supervised information. The one-hot label can’t accurately define the similarity of two images, which is important for image retrieval. In this paper, we propose an effective method, Deep Transfer Hashing( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DTH</i> ) which uses the knowledge from teacher model as the supervised information. Inspired by knowledge distillation for model compression and deep hashing for fast image retrieval, we transfer the knowledge from a complex convolutional neural network(teacher) to a small neural network(student) which is used for fast image retrieval. The distance of the knowledge from teacher model can indicate the similarity of images. By minimizing the hashing codes distribution between the hashing layers of teacher model and student model, we can improve the retrieval performance. And we also evaluate the performance of the compressed model at inference stage. We test our method on widely used datasets CIFAR-10 and NUS-WIDE and we compare our method with other state-of-the-art methods in image retrieval domain. The experimental results show that our method can improve the image retrieval baseline by a large margin and better than other methods.