Abstract
Concept detection for semantic annotation of video fragments (e.g. keyframes) is a popular and challenging problem. A variety of visual features is typically extracted and combined in order to learn the relation between feature-based keyframe representations and semantic concepts. In recent years the available pool of features has increased rapidly, and features based on deep convolutional neural networks in combination with other visual descriptors have significantly contributed to improved concept detection accuracy. This work proposes an algorithm that dynamically selects, orders and combines many base classifiers, trained independently with different feature-based keyframe representations, in a cascade architecture for video concept detection. The proposed cascade is more accurate and computationally more efficient, in terms of classifier evaluations, than state-of-the-art classifier combination approaches.
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This work was supported by the European Commission under contract FP7-600826 ForgetIT.
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Markatopoulou, F., Mezaris, V., Patras, I. (2016). Ordering of Visual Descriptors in a Classifier Cascade Towards Improved Video Concept Detection. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_73
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DOI: https://doi.org/10.1007/978-3-319-27671-7_73
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