DocumentCode :
1196922
Title :
Generative probabilistic models for multimedia retrieval: query generation against document generation
Author :
Westerveld, T. ; de Vries, A.P.
Author_Institution :
CWI/INSI, Amsterdam, Netherlands
Volume :
152
Issue :
6
fYear :
2005
Firstpage :
852
Lastpage :
858
Abstract :
This paper presents the use of generative probabilistic models for multimedia retrieval. Gaussian mixture models are estimated to describe the visual content of images (or video) and are explored in different ways of using them for retrieval. So-called query generation (how likely is the query given the document model) and document generation (how likely is the document given the query model) approaches are considered and how both fit in a common probabilistic framework is explained. Query generation is shown to be theoretically superior, and confirmed experimentally on the Trecvid search task. However, it is found that in some cases a document generation approach gives better results. Especially in the cases where queries are narrow and visual results are combined with textual results, the document generation approach seems to be better at setting a visual context than the query generation variant.
Keywords :
Gaussian processes; image retrieval; multimedia systems; probability; video signal processing; Gaussian mixture model; document generation approach; generative probabilistic model; multimedia retrieval; query generation; video signal processing; visual image content;
fLanguage :
English
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
Publisher :
iet
ISSN :
1350-245X
Type :
jour
DOI :
10.1049/ip-vis:20045196
Filename :
1520872
Link To Document :
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