Abstract
Active learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. It involves however a large number of computations. In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. The experimental result show that the best performance (MAP) is reached when 15-30% of the corpus is annotated and the new method can achieve almost the same precision while saving 50 to 63% of the computation time.
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Safadi, B., Tong, Y., Quénot, G. (2011). Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_23
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DOI: https://doi.org/10.1007/978-3-642-17832-0_23
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