CNN-VWII: An efficient approach for large-scale video retrieval by image queries

被引：41

作者：

Zhang, Chengyuan ^{[1
]}

Lin, Yunwu ^{[1
]}

Zhu, Lei ^{[1
]}

Liu, Anfeng ^{[1
]}

Zhang, Zuping ^{[1
]}

Huang, Fang ^{[1
]}

机构：

[1] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2019年 / 123卷

基金：

中国国家自然科学基金;

关键词：

943.3 Special Purpose Instruments;

D O I：

10.1016/j.patrec.2019.03.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we propose a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the stateof-the-art techniques with similar accuracy. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：82 / 88

页数：7