The COST292 experimental framework for TRECVID 2007

Date
2007
Advisor
Instructor
Source Title
TRECVID 2007 workshop participants notebook papers
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Publisher
National Institute of Standards and Technology
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Language
English
Type
Conference Paper
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Abstract

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features.

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Keywords
Ant colony optimization, Feature extraction, Particle swarm optimization (PSO), Semantics, User interfaces, Ant colony optimisation, Bayesian classifier, High-level feature extractions, Interactive retrieval, Latent Semantic Analysis, Low level descriptors, Particle swarm optimisation, Video summarisation, Image retrieval
Citation
Published Version (Please cite this version)