Aphash: Anchor-Based Probability Hashing for Image Retrieval

Abstract

In this paper, we propose a novel unsupervised hashing method called Anchor-based Probability Hashing (APHash) to preserve the similarities by exploiting the distribution of data points. In particular, distances are transformed into probabilities in both original and hash code spaces. Our method aims to learn hash codes which minimize the mismatch between probability distributions of these two spaces. To address the high complexity issue, our method randomly selects a set of anchors and constructs asymmetric probability matrices. In this way, APHash can make use of the correlation between anchors and data points to learn hash codes more efficiently. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed APHash method, outperforming state-of-the-art hashing approaches in the application of image retrieval.