Large-Scale Video Retrieval via Deep Local Convolutional Features
Joint Authors
Ji, Yimu
Zhang, Chen
Hu, Bin
Suo, Yucong
Zou, Zhiqiang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-09
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos.
A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency.
Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy.
Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced.
The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database.
The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.
American Psychological Association (APA)
Zhang, Chen& Hu, Bin& Suo, Yucong& Zou, Zhiqiang& Ji, Yimu. 2020. Large-Scale Video Retrieval via Deep Local Convolutional Features. Advances in Multimedia،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1126706
Modern Language Association (MLA)
Zhang, Chen…[et al.]. Large-Scale Video Retrieval via Deep Local Convolutional Features. Advances in Multimedia No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1126706
American Medical Association (AMA)
Zhang, Chen& Hu, Bin& Suo, Yucong& Zou, Zhiqiang& Ji, Yimu. Large-Scale Video Retrieval via Deep Local Convolutional Features. Advances in Multimedia. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1126706
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126706