Hidden multi-distance loss-based full-convolution hashing

Abstract

To improve retrieval efficiency and quality, learning to hash has been widely used in approximate nearest neighbor queries. Deep learning is characterized by high precision in extracting data features; therefore, deep-learning-based hashing has attracted more attention. Existing methods have some weaknesses, such as complex training and losing spatial information. We design a new deep hashing algorithm named HLFH, which is very simple technique but achieves amazingly good performance. HLFH is optimized and improved in two aspects: network structure and hashing loss. Concerning network structure, a new full convolutional hashing network is proposed to preserve spatial information of features. A smooth activation function is used in the hashing layer to reduce the quantization error. Concerning hashing loss, the semantic information of data is then used to generate binary codes by hidden multi-distance loss, i.e., combination of triplet loss and quadruplet loss. With these two new techniques, our method is more accurate than many other state-of-the-art methods.