Survey on Hash Learning for Large-scale Image Retrieval

As image data grows explosively on the Internet and image application fields widen, the demand for large-scale image retrieval is increasing greatly. Hash learning provides significant storage and retrieval efficiency for large-scale image retrieval and has attracted intensive research interest in recent years. Existing surveys on hash learning are confronted with the problems of weak timeliness and unclear technical routes. Specifically, they mainly conclude the hashing methods proposed five to ten years ago, and few of them conclude the relationship between the components of hashing methods. In view of this, this study makes a comprehensive survey on hash learning for large-scale image retrieval by reviewing the hash learning literature published in the past twenty years. First, the technical route of hash learning and the key components of hashing methods are summarized, including loss function, optimization strategy, and out-of-sample extension. Second, hashing methods for image retrieval are classified into two categories: unsupervised hashing methods and supervised ones. For each category of hashing methods, the research status and evolvement process are analyzed. Third, several image benchmarks and evaluation metrics are introduced, and the performance of some representative hashing methods is analyzed through comparative experiments. Finally, the future research directions of hash learning are summarized considering its limitations and new challenges. © 2025 Chinese Academy of Sciences. All rights reserved.