A Novel Approach to Spatio-Temporal Video Analysis and Retrieval

Authors:
Sameer Singh;Wei Ren;Maneesha Singh
Affiliations:
Research School of Informatics, Loughborough University, Loughborough, United Kingdom LE11 3TU;Faulty of Computer and Information Engineering, ShenZhen Graduate School of Peking University, ShenZhen, China 518055;Research School of Informatics, Loughborough University, Loughborough, United Kingdom LE11 3TU
Venue:
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Year:
2009

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Abstract

In this paper, we propose a novel Spatio-Temporal Analysis and Retrieval model to extract attributes for video category classification. First, the spatial relationships and temporal nature of the video object in a frame is coded as the sequence of binary string ---VRstring. Then, the similarity between shots is matched as sequential features in hyperspaces. The results show that VRstring allows us to define higher level semantic features capturing the main narrative structures of the video. We also compare our algorithm with state of the art longest common substring finding video retrieval model by Adjeroh et.al.[1] on the Minerva international video benchmark.