Semi-Supervised Deep Hashing with a Bipartite Graph

Semi-Supervised Deep Hashing with a Bipartite Graph

Xinyu Yan, Lijun Zhang, Wu-Jun Li

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3238-3244. https://doi.org/10.24963/ijcai.2017/452

Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, DHBG is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of DHBG to support out-of-sample extensions. Experimental results on real datasets show that our DHBG outperforms state-of-the-art hashing methods.
Keywords:
Machine Learning: Machine Learning
Machine Learning: Semi-Supervised Learning
Machine Learning: Deep Learning