Bidirectional Discrete Matrix Factorization Hashing for Image Search

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency.