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CIVR
2005
Springer

A Comparison of Score, Rank and Probability-Based Fusion Methods for Video Shot Retrieval

11 years 3 months ago
A Comparison of Score, Rank and Probability-Based Fusion Methods for Video Shot Retrieval
It is now accepted that the most e ective video shot retrieval is based on indexing and retrieving clips using multiple, parallel modalities such as text-matching, image-matching and feature matching and then combining or fusing these parallel retrieval streams in some way. In this paper we investigate a range of fusion methods for combining based on multiple visual features (colour, edge and texture), for combining based on multiple visual examples in the query and for combining multiple modalities (text and visual). Using three TRECVid collections and the TRECVid search task, we speci cally compare fusion methods based on normalised score and rank that use either the average, weighted average or maximum of retrieval results from a discrete Jelinek-Mercer smoothed language model. We also compare these results with a simple probability-based combination of the language model results that assumes all features and visual examples are fully independent.
Kieran McDonald, Alan F. Smeaton
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIVR
Authors Kieran McDonald, Alan F. Smeaton
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