Efficient near-duplicate keyframe retrieval with visual language models
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...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2007
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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