Adaptive Asymmetric Label-guided Hashing for Multimedia Search

TL;DR

This paper introduces A2LH, an efficient two-step hashing method that uses semantic labels as an intermediary to improve cross-modal multimedia retrieval, addressing semantic errors and quantization issues in existing methods.

Contribution

A2LH employs a novel association model and an efficient discrete optimization algorithm, advancing the accuracy and efficiency of multimedia hash learning.

Findings

Achieves superior retrieval performance compared to baseline methods.

Effectively reduces semantic errors in multi-label data.

Addresses quantization errors with a new optimization algorithm.

Abstract

With the rapid growth of multimodal media data on the Web in recent years, hash learning methods as a way to achieve efficient and flexible cross-modal retrieval of massive multimedia data have received a lot of attention from the current Web resource retrieval research community. Existing supervised hashing methods simply transform label information into pairwise similarity information to guide hash learning, leading to a potential risk of semantic error in the face of multi-label data. In addition, most existing hash optimization methods solve NP-hard optimization problems by employing approximate approximation strategies based on relaxation strategies, leading to a large quantization error. In order to address above obstacles, we present a simple yet efficient Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search. Specifically, A2LH is a two-step hashing method. In the first step, we design an association representation model between the different modality representations and semantic label representation separately, and use the semantic label representation as an intermediate bridge to solve the semantic gap existing between different modalities. In addition, we present an efficient discrete optimization algorithm for solving the quantization error problem caused by relaxation-based optimization algorithms. In the second step, we leverage the generated hash codes to learn the hash mapping functions. The experimental results show that our proposed method achieves optimal performance on all compared baseline methods.