By Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
The booklet deals a well timed photograph of neural community applied sciences as an important component to sizeable info analytics systems. It promotes new advances and examine instructions in effective and cutting edge algorithmic ways to reading great info (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on diversified computing systems (e.g. neuromorphic, photographs processing devices (GPUs), clouds, clusters); and massive information analytics purposes to resolve real-world difficulties (e.g. climate prediction, transportation, strength management). The booklet, which studies at the moment version of the resorts convention on gigantic info, hung on October 23–25, 2016, in Thessaloniki, Greece, depicts a fascinating collaborative event of neural networks with substantial info and different studying technologies.
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Extra resources for Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
K is the number of available image modalities: l stands for luminance, h for color hue, o for optical ﬂow magnitude and e for edge map. Each Vik is a digitized 8-bit image with a resolution of M × N pixels. The presented framework operates by initially computing a set of low-level, L-dimensional description vectors at each Vik . For a given i, a single set of Compact Video Description and Representation 21 descriptors Di is subsequently derived from all image modalities, by simply concatenating corresponding vectors computed for diﬀerent values of k.
Subsequently, a set of frames that are closest to each cluster centroid are initially selected as key-frames. g. by applying clustering at shotlevel. Typically, a number of the extracted key-frames are ﬁltered out to reduce redundancy and the rest are presented in temporal order. Although the above approaches are oriented towards generic video input, methods exploiting video type-speciﬁc information have also been proposed. In surveillance videos, temporal segmentation (shot boundaries detection ) is not a viable option due to the lack of cuts, therefore motion detection is employed in order to create summaries that contain sets of object actions, like pedestrian walking.
1, provided the basis for a novel local descriptor, called Local Moments Descriptor (LMoD). LMoD operates in the manner presented below. To describe a given image block B with a dimension of b × b pixels, B is recursively partitioned into quadrant sub-blocks using the frame partitioning scheme of the global descriptor case. For each sub-block, the 18-dimensional Compact Video Description and Representation 23 description vector of the extended FMoD descriptor is computed and all such vectors are concatenated into an 18S(d)-dimensional block description vector, where S(d) is given by Eq.