Efficient Binary Multi-view Subspace Learning for Instance-Level Image Retrieval

The existing hashing methods mainly handle either the feature based nearest-neighbour search or the category-level image retrieval, whereas a few efforts are devoted to instance retrieval problem. Besides, although multi-view hashing methods are capable of exploring the complementarity among multiple heterogeneous visual features, they heavily rely on massive labeled training data, and somewhat affects the real-world applications. In this paper, we propose a binary multi-view fusion framework for directly recovering a latent Hamming subspace from the multi-view features. More specifically, the multi-view subspace reconstruction and the binary quantization are integrated in a unified framework so as to minimize the discrepancy between the original multi-view high-dimensional Euclidean space and the resulting compact Hamming subspace. In addition, our method is amenable to efficient iterative optimization for learning a compact similarity-preserving binary code. The resulting binary codes demonstrate significant advantage in retrieval precision and computational efficiency at the cost of limited memory footprint. More importantly, our method is essentially an unsupervised learning scheme without any labeled data involved, and thus can be used in the cases when the supervised information is unavailable or insufficient. Experiments on public benchmark and large-scale datasets reveal that our method achieves competitive retrieval performance comparable to the state-of-the-art and has excellent scalability in large-scale scenario.