By Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
The e-book bargains a well timed photograph of neural community applied sciences as an important component to tremendous facts analytics structures. It promotes new advances and study instructions in effective and cutting edge algorithmic methods to studying great information (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on diverse computing systems (e.g. neuromorphic, pictures processing devices (GPUs), clouds, clusters); and massive info analytics purposes to unravel real-world difficulties (e.g. climate prediction, transportation, power management). The publication, which reviews at the moment version of the hotels convention on substantial information, hung on October 23–25, 2016, in Thessaloniki, Greece, depicts an attractive collaborative experience of neural networks with great info and different studying technologies.
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Additional info for Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005) 8. : Max-margin dictionary learning for multiclass image categorization. , Paragios, N. ) ECCV 2010. LNCS, vol. 6314, pp. 157–170. Springer, Heidelberg (2010). doi:10. 1007/978-3-642-15561-1 12 9. : Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150– 1157 (1999) 10.
In [5,6]), were evaluated on the presented IMPART dataset, using the IR metric and the K-Means++ algorithm  for frame clustering, as the main summarization method. Other clustering algorithms have been tested and shown to provide similar results. The method in  was employed for optical ﬂow estimation. The Laplace operator was used for deriving the edge map image modality, after 3 × 3 median ﬁltering for noise suppression. , the number of requested extracted key-frames per video, is a user-provided parameter which controls the grain of summarization.
Gr Abstract. A compact framework is presented for the description and representation of videos depicting human activities, with the goal of enabling automated large-volume video summarization for semantically meaningful key-frame extraction. The framework is structured around the concept of per-frame visual word histograms, using the popular Bag-of-Features approach. , frame clustering. In all cases, several image modalities are exploited (luminance, hue, edges, optical ﬂow magnitude) in order to simultaneously capture information about the depicted shapes, colors, lighting, textures and motions.