Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2019
Abstract
In this paper, the VIREO team video retrieval tool is described in details. As learned from Video Browser Showdown (VBS) 2018, the visualization of video frames is a critical need to improve the browsing effectiveness. Based on this observation, a hierarchical structure that represents the video frame clusters has been built automatically using k-means and self-organizing-map and used for visualization. Also, the relevance feedback module which relies on real-time supportvector-machine classification becomes unfeasible with the large dataset provided in VBS 2019 and has been replaced by a browsing module with pre-calculated nearest neighbors. The preliminary user study results on IACC.3 dataset show that these modules are able to improve the retrieval accuracy and efficiency in real-time video search system.
Keywords
Video browser showdown, Video retrieval, Video visualization
Discipline
Data Storage Systems | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
MultiMedia Modeling: 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8-11: Proceedings
Volume
11296
First Page
609
Last Page
615
ISBN
9783030057152
Identifier
10.1007/978-3-030-05716-9_54
Publisher
Springer
City or Country
Cham
Citation
NGUYEN, Phuong Anh; NGO, Chong-wah; FRANCIS, Danny; and HUET, Benoit.
Vireo @ video browser showdown 2019. (2019). MultiMedia Modeling: 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8-11: Proceedings. 11296, 609-615.
Available at: https://ink.library.smu.edu.sg/sis_research/6640
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007%2F978-3-030-05716-9_54