Abstract
As digital video databases grow, so grows the problem of effectively navigating through them. In this paper we propose a novel content-based video retrieval approach to searching such video databases, specifically those involving human actions, incorporating spatio-temporal localization. We outline a novel, highly efficient localization model that first performs temporal localization based on histograms of evenly spaced time-slices, then spatial localization based on histograms of a 2-D spatial grid. We further argue that our retrieval model, based on the aforementioned localization, followed by relevance ranking, results in a highly discriminative system, while remaining an order of magnitude faster than the current state-of-the-art method. We also show how relevance feedback can be applied to our localization and ranking algorithms. As a result, the presented system is more directly applicable to real-world problems than any prior content-based video retrieval system.
Original language | English |
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Pages (from-to) | 504-512 |
Number of pages | 9 |
Journal | IEEE Transactions on Circuits and Systems for Video Technology |
Volume | 24 |
Issue number | 3 |
Early online date | 6 Aug 2013 |
DOIs | |
Publication status | Published - 1 Mar 2014 |
Keywords
- Human actions
- relevance feedback
- spatiotemporal localization
- video retrieval