Feature adaptive multi-view hash for image search

With the rapid development of network technology and small handheld devices, the amount of data has significantly increased and various kinds of data can be supplied to us at the same time. Recently, hashing technology has become popular in executing large-scale similarity search and image matching tasks. However, most of the prior hashing methods are mainly focused on the choice of the high-dimensional feature descriptor for learning effective hashing functions. In practice, real world image data collected from multiple scenes cannot be descriptive enough by using a single type of feature. Recently, several unsupervised multi-view hashing learning methods have been proposed based on matrix factorization, anchor graph and metric learning. However, large quantization error will be introduced via a sign function and the robustness of multi-view hashing is ignored. In this paper we present a novel feature adaptive multi-view hashing (FAMVH) method based on a robust multi-view quantization framework. The proposed method is evaluated on three large-scale benchmarks CIFAR-10, CIFAR-20 and Caltech-256 for approximate nearest neighbor search task. The experimental results show that our approach can achieve the best accuracy and efficiency in the three large-scale datasets.