Abstract
Classification is a major task in many applications and in particular for automatic semantic-based video content indexing and retrieval. In this paper, we focus on the challenging task of classifier output fusion. It is a necessary step to efficiently estimate the semantic content of video shots from multiple cues. We propose to fuse the numeric information provided by multiple classifiers in the framework of evidence logic. For this purpose, an improved version of RBF network based on Evidence Theory (NN-ET) is proposed. Experiments are conducted in the framework of TrecVid high level feature extraction task that consists of ordering shots with respect to their relevance to a given semantic class.
The work presented here is funded by France Télécom R&D under CRE 46134752.
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Benmokhtar, R., Huet, B. (2006). Neural Network Combining Classifier Based on Dempster-Shafer Theory for Semantic Indexing in Video Content. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69429-8_20
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DOI: https://doi.org/10.1007/978-3-540-69429-8_20
Publisher Name: Springer, Berlin, Heidelberg
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