Efficient near-duplicate keyframe retrieval with visual language models

• Main Authors: WU, Xiao, ZHAO, Wan-Lei, NGO, Chong-wah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6603; https://ink.library.smu.edu.sg/context/sis_research/article/7606/viewcontent/Efficient_Near_Duplicate_Keyframe_Retrie.pdf
• Institution: Singapore Management University

Near-duplicate keyframe retrieval is a critical task for video similarity measure, video threading and tracking. In this paper, instead of using expensive point-to-point matching on keypoints, we investigate the visual language models built on visual keywords to speed up the near-duplicate keyframe retrieval. The main idea is to estimate a visual language model on visual keywords for each keyframe and compare keyframes by the likelihood of their visual language models. Experiments on a subset of TRECVID-2004 video corpus show that visual language models built on visual keywords demonstrate promising performance for near-duplicate keyframe retrieval, which greatly speed up the retrieval speed although sacrifice a little performance compared to expensive point-to-point matching.