Hadamard Matrix Guided Online Hashing
Mingbao Lin, Rongrong Ji, Hong Liu, Xiaoshuai Sun, Shen Chen, Qi Tian

TL;DR
This paper introduces Hadamard Matrix Guided Online Hashing (HMOH), a novel supervised online hashing method that leverages Hadamard matrices to improve accuracy and efficiency in large-scale streaming image data.
Contribution
The paper proposes a new online hashing scheme using Hadamard matrices to eliminate the need for strong constraints and accelerate training, outperforming existing methods.
Findings
HMOH achieves superior accuracy compared to state-of-the-art methods.
HMOH demonstrates improved training efficiency and scalability.
Extensive experiments validate the effectiveness of the proposed approach.
Abstract
Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: First, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drop. Second, large amounts of training batches are required to learn the up-to-date hash functions, which largely increase the learning complexity. To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this paper. Our key innovation lies in introducing…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Image Retrieval and Classification Techniques
