Learning-Based Hashing for ANN Search: Foundations and Early Advances

2025-10-05

AI Summary

Approximate nearest neighbor (ANN) search remains a core challenge in large-scale cross-modal retrieval. This paper systematically surveys the early development of learning-based hashing methods, focusing on data-driven optimization of projection functions and quantization strategies to map high-dimensional features into compact binary codes enabling efficient similarity computation in Hamming space. Distinguishing itself from random hashing, the survey categorizes approaches into supervised, unsupervised, and semi-supervised paradigms, covering key directions including multi-bit encoding, adaptive thresholding, and cross-modal extensions. It distills their theoretical foundations and design principles, elucidating the fundamental trade-offs among accuracy, efficiency, and generalizability. Furthermore, it establishes a structured conceptual framework that clarifies the applicability boundaries and open challenges of early models. By doing so, the work provides both a theoretical reference and an evolutionary roadmap for future research on interpretable, robust, and multimodal hashing.

Technology Category

• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search
• Computer Vision: Representation Learning for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Approximate Nearest Neighbour (ANN) search is a fundamental problem in information retrieval, underpinning large-scale applications in computer vision, natural language processing, and cross-modal search. Hashing-based methods provide an efficient solution by mapping high-dimensional data into compact binary codes that enable fast similarity computations in Hamming space. Over the past two decades, a substantial body of work has explored learning to hash, where projection and quantisation functions are optimised from data rather than chosen at random. This article offers a foundational survey of early learning-based hashing methods, with an emphasis on the core ideas that shaped the field. We review supervised, unsupervised, and semi-supervised approaches, highlighting how projection functions are designed to generate meaningful embeddings and how quantisation strategies convert these embeddings into binary codes. We also examine extensions to multi-bit and multi-threshold models, as well as early advances in cross-modal retrieval. Rather than providing an exhaustive account of the most recent methods, our goal is to introduce the conceptual foundations of learning-based hashing for ANN search. By situating these early models in their historical context, we aim to equip readers with a structured understanding of the principles, trade-offs, and open challenges that continue to inform current research in this area.

Problem

• Surveying early learning-based hashing methods for ANN search
• Exploring projection and quantization functions optimized from data
• Introducing conceptual foundations and historical context of hashing

Innovation

• Learning-based hashing optimizes projection and quantization functions
• Methods map high-dimensional data into compact binary codes
• Supervised and unsupervised approaches generate meaningful binary embeddings