Abstract
Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Beyond the systems’ implementations issues, semantic indexing is strongly dependent upon the size and quality of the training examples. In this paper, we describe the collaborative annotation system used to annotate the High Level Features (HLF) in the development set of TRECVID 2007. This system is web-based and takes advantage of Active Learning approach. We show that Active Learning allows simultaneously getting the most useful information from the partial annotation and significantly reducing the annotation effort per participant relatively to previous collaborative annotations.
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Ayache, S., Quénot, G. (2008). Video Corpus Annotation Using Active Learning. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_19
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DOI: https://doi.org/10.1007/978-3-540-78646-7_19
Publisher Name: Springer, Berlin, Heidelberg
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