Wang, Lei; Song, Dawei and Elyan, Eyad
(2012).
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Abstract
Most of the state-of-art approaches to Query-by-Example (QBE) video retrieval are based on the Bag-of-visual-Words (BovW) representation of visual content. It, however, ig- nores the spatial-temporal information, which is important for similarity measurement between videos. Direct incorpo- ration of such information into the video data representa- tion for a large scale data set is computationally expensive in terms of storage and similarity measurement. It is also static regardless of the change of discriminative power of vi- sual words with respect to di↵erent queries. To tackle these limitations, in this paper, we propose to discover Spatial- Temporal Correlations (STC) imposed by the query exam- ple to improve the BovW model for video retrieval. The STC, in terms of spatial proximity and relative motion co- herence between di↵erent visual words, is crucial to identify the discriminative power of the visual words. We develop a novel technique to emphasize the most discriminative visual words for similarity measurement, and incorporate this STC-based approach into the standard inverted index archi- tecture. Our approach is evaluated on the TRECVID2002 and CC WEB VIDEO datasets for two typical QBE video retrieval tasks respectively. The experimental results demon- strate that it substantially improves the BovW model as well as a state of the art method that also utilizes spatial- temporal information for QBE video retrieval.
| Item Type: | Conference or Workshop Item |
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| Copyright Holders: | 2012 ACM |
| Extra Information: | ISBN: 978-1-4503-1156-4 |
| Keywords: | spatial-temporal correlation; content based video retrieval; query-by-example; Bag-of-Visual-Words |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications |
| Related URLs: | |
| Item ID: | 34678 |
| Depositing User: | Dawei Song |
| Date Deposited: | 16 Oct 2012 12:40 |
| Last Modified: | 07 Dec 2018 23:09 |
| URI: | http://oro.open.ac.uk/id/eprint/34678 |
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