Discriminative Codebook Hashing for Supervised Video Retrieval.

Xiaoman Bian,Xiaonan Luo,Zhenbing Liu,Chen Chen,Rushi Lan,Kuei-Kuei Lai,Xiaoqin Wang,Nian Zhang

doi:10.1155/2021/5845094

Abstract

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback–Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.

Highlights

Under the condition of the increase in smartphones, the amount of video data has shown an explosive growth trend [1,2,3]
Zhang et al [22] developed a convergence-preserving parametric learning algorithm, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models
We will give the detailed analysis of all results of the three datasets in the following parts

Summary

Introduction

Under the condition of the increase in smartphones, the amount of video data has shown an explosive growth trend [1,2,3]. Data-independent approaches learn binary codes without data information but through random space projection. E most representative algorithm is local sensitive hashing (LSH) [15], which generates huge redundant information using random mapping and obtains satisfactory performance with long hash codes. Data-dependent hash methods [16,17,18], which can be divided into unsupervised hashing and supervised hashing, are proposed to generate more efficient hash codes by maintaining the neighborhood structure between data. Liu et al [23] proposed kernel supervised hashing (KSH) by applying kernel-based formulas to accommodate linearly inseparable data and designed a greedy algorithm to solve the hash function optimization problem

Methods

Results

Conclusion