DEEP SELF-LEARNING HASHING FOR IMAGE RETRIEVAL

With the advances in deep learning, deep-hashing methods have achieved promising results for image retrieval. However, the problem of the distribution gap between training data and test data remains unsolved. Existing methods rely too much on manually labeled information to construct similarity matrices as supervision signals and focus less on pre-trained networks that can extract semantic information. This limits the generalization performance of the network and produces less discriminative hash codes. In this paper, we propose a novel hashing method, deep self-learning hashing (DSLH), that uses a self-learning strategy with labels constructed using the pre-trained features to enhance the embedded representation of the hash codes. Furthermore, we develop an improved loss function that preserves the similarity of the hash codes while reducing the quantization loss and ensuring the balance of the hash codes. Our analysis and experimental results demonstrate that, compared with recent image-retrieval methods, our method can achieve greater retrieval performance on two benchmark datasets: CIFAR-10 and NUS-WIDE.