Abstract
In this paper a short overview on recent research efforts for digital media analysis and description using neural networks is given. Neural networks are very powerful in analyzing, representing and classifying digital media content through various architectures and learning algorithms. Both unsupervised and supervised algorithms can be used for digital media feature extraction. Digital media representation can be done either in a synaptic level or at the output level. The specific problem that is used as a case study for digital media analysis is the human-centered video analysis for activity and identity recognition. Several neural network topologies, such as self organizing maps, independent subspace analysis, multi-layer perceptrons, extreme learning machines and deep learning architectures are presented and results on human activity recognition are reported.
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Tefas, A., Iosifidis, A., Pitas, I. (2013). Neural Networks for Digital Media Analysis and Description. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_1
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DOI: https://doi.org/10.1007/978-3-642-41013-0_1
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