Supervised Online Hashing via Hadamard Codebook Learning

04/28/2019
by   Mingbao Lin, et al.
0

In recent years, binary code learning, a.k.a hashing, has received extensive attention in large-scale multimedia retrieval. It aims to encode high-dimensional data points to binary codes, hence the original high-dimensional metric space can be efficiently approximated via Hamming space. However, most existing hashing methods adopted offline batch learning, which is not suitable to handle incremental datasets with streaming data or new instances. In contrast, the robustness of the existing online hashing remains as an open problem, while the embedding of supervised/semantic information hardly boosts the performance of the online hashing, mainly due to the defect of unknown category numbers in supervised learning. In this paper, we proposed an online hashing scheme, termed Hadamard Codebook based Online Hashing (HCOH), which aims to solve the above problems towards robust and supervised online hashing. In particular, we first assign an appropriate high-dimensional binary codes to each class label, which is generated randomly by Hadamard codes to each class label, which is generated randomly by Hadamard codes. Subsequently, LSH is adopted to reduce the length of such Hadamard codes in accordance with the hash bits, which can adapt the predefined binary codes online, and theoretically guarantee the semantic similarity. Finally, we consider the setting of stochastic data acquisition, which facilitates our method to efficiently learn the corresponding hashing functions via stochastic gradient descend (SGD) online. Notably, the proposed HCOH can be embedded with supervised labels and it not limited to a predefined category number. Extensive experiments on three widely-used benchmarks demonstrate the merits of the proposed scheme over the state-of-the-art methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2019

Supervised Online Hashing via Similarity Distribution Learning

Online hashing has attracted extensive research attention when facing st...
research
11/19/2016

Ordinal Constrained Binary Code Learning for Nearest Neighbor Search

Recent years have witnessed extensive attention in binary code learning,...
research
11/27/2018

A Scalable Optimization Mechanism for Pairwise based Discrete Hashing

Maintaining the pair similarity relationship among originally high-dimen...
research
10/10/2020

Making Online Sketching Hashing Even Faster

Data-dependent hashing methods have demonstrated good performance in var...
research
05/11/2019

Hadamard Matrix Guided Online Hashing

Online image hashing has received increasing research attention recently...
research
01/29/2019

Towards Optimal Discrete Online Hashing with Balanced Similarity

When facing large-scale image datasets, online hashing serves as a promi...
research
12/02/2014

Hashing on Nonlinear Manifolds

Learning based hashing methods have attracted considerable attention due...

Please sign up or login with your details

Forgot password? Click here to reset