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Conference Papers Year : 2012

Active Cleaning for Video Corpus Annotation


In this paper, we have described the Active Cleaning approach that was used to complete the active learning approach in the TRECVID collaborative annotation. It consists of using a classification system to select the samples to be re-annotated in order to improve the quality of the annotations. We have evaluated the actual impact of our active cleaning approach on the TRECVID 2007 collection, using full annotations collected from the TRECVID collaborative annotations and the MCG-ICT-CAS annotations. From our experiments, a significant improvement of our annotation systems performance was observed when selecting a small fraction of samples to be re-annotated by our cleaning strategy, denoted as Cross-Val, than using the same fraction to annotate more new samples. Furthermore, it shows that higher performance can be reached with double annotations of 10% of negative samples, or 5% of all the annotated samples that were selected by the proposed cleaning strategy.
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hal-00767345 , version 1 (19-12-2012)



Bahjat Safadi, Stéphane Ayache, Georges Quénot. Active Cleaning for Video Corpus Annotation. MMM 2012 - International MultiMedia Modeling Conference, Jan 2012, Klagenfurt, Austria. pp.518-528, ⟨10.1007/978-3-642-27355-1_48⟩. ⟨hal-00767345⟩
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