Multiview Alignment Hashing for Efficient Image Search.Hashing is a popular and efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension.

For most hashing methods, the performance of retrieval heavily depends on the choice of the high-dimensional feature descriptor. Thus, how to combine multiple representations for learning effective hashing functions is an imminent task.

In this paper, we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization, which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data.In particular, we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy.Since the raised problem is regarded as nonconvex and discrete, our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution.

Learning discriminatie embedding has been a critical problem in many fields of information processing and analysis, such as object recognition, image/video retrieval and visual detection.Among them, scalable retrieval of similar visual information is attractive, since with the advances of computer technologies and the development of the World Wide Web, a huge amount of digital data has been generated and applied.