Scalable feature-based video retrieval for mobile devices
| dc.contributor.author | Morbee, Marleen | en_GB |
| dc.contributor.author | Velisavljević, Vladan | en_GB |
| dc.contributor.author | Mrak, Marta | en_GB |
| dc.contributor.author | Philips, Wilfried | en_GB |
| dc.date.accessioned | 2013-05-22T11:13:38Z | |
| dc.date.available | 2013-05-22T11:13:38Z | |
| dc.date.issued | 2009 | |
| dc.identifier.citation | Morbee, M., Velisavljevic, V., Mrak, M. and Philips, W. (2009) 'Scalable feature-based video retrieval for mobile devices', ACM Int. Conf. on Internet Multimedia Computing and Service (ICIMCS-2009), Kunming, Yunnan, China, November 2009. Kunming: ACM, pp.3-9 | en_GB |
| dc.identifier.isbn | 9781605588407 | |
| dc.identifier.doi | 10.1145/1734605.1734611 | |
| dc.identifier.uri | http://hdl.handle.net/10547/292568 | |
| dc.description.abstract | The authors present a novel method for video retrieval on mobile devices. The target scenario is the following: features are extracted from a captured query video at the user side and transmitted to a remote server; then, video retrieval is applied within a stored database at the server side and relevant information about the query is returned to the user. In particular, we focus on the user side and propose a scalable method for video feature extraction and encoding taking into consideration the processing capabilities of the device and available bandwidth. Despite these constraints, the first results show a promising accuracy of retrieval. | |
| dc.language.iso | en | en |
| dc.publisher | ACM | en_GB |
| dc.relation.url | http://dl.acm.org/citation.cfm?id=1734611 | en_GB |
| dc.subject | information systems | en_GB |
| dc.subject | information retrieval | en_GB |
| dc.title | Scalable feature-based video retrieval for mobile devices | en |
| dc.type | Conference papers, meetings and proceedings | en |
| html.description.abstract | The authors present a novel method for video retrieval on mobile devices. The target scenario is the following: features are extracted from a captured query video at the user side and transmitted to a remote server; then, video retrieval is applied within a stored database at the server side and relevant information about the query is returned to the user. In particular, we focus on the user side and propose a scalable method for video feature extraction and encoding taking into consideration the processing capabilities of the device and available bandwidth. Despite these constraints, the first results show a promising accuracy of retrieval. |
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