Vireo @ video browser showdown 2019

• Main Authors: NGUYEN, Phuong Anh, NGO, Chong-wah, FRANCIS, Danny, HUET, Benoit
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Video browser showdown; Video retrieval; Video visualization; Data Storage Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6640; https://ink.library.smu.edu.sg/context/sis_research/article/7643/viewcontent/2019_VIREOVideoBrowserShowdown2019.pdf
• Institution: Singapore Management University

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real-time supportvector-machine classification becomes unfeasible with the large dataset provided in VBS 2019 and has been replaced by a browsing module with pre-calculated nearest neighbors.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.