Learning compact binary codes via pairwise correlation reconstruction

Due to the explosive growth of visual data and the raised urgent needs for more efficient nearest neighbor search methods, hashing methods have been widely studied in recent years. However, parameter optimization of the hash function in most available approaches is tightly coupled with the form of the function itself, which makes the optimization difficult and consequently affects the similarity preserving performance of hashing. To address this issue, we propose a novel pairwise correlation reconstruction framework for learning compact binary codes flexibly. Firstly, each data point is projected into a metric space and represented as a vector encoding the underlying local and global structure of the input space. The similarities of the data are then measured by the pairwise correlations of the learned vectors, which are represented as Euclidean distances. Afterwards, in order to preserve the similarities maximally, the optimal binary codes are learned by reconstructing the pairwise correlations. Experimental results are provided and analyzed on four commonly used benchmark datasets to demonstrate that the proposed method achieves the best nearest neighbor search performance comparing with the state-of-the-art methods.