Advances in Big Data: Proceedings of the 2nd INNS Conference by Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim

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 section of substantial information analytics structures. It promotes new advances and learn instructions in effective and leading edge algorithmic methods to reading huge facts (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on various computing structures (e.g. neuromorphic, pictures processing devices (GPUs), clouds, clusters); and massive information analytics purposes to unravel real-world difficulties (e.g. climate prediction, transportation, power management). The e-book, which studies at the moment version of the lodges convention on colossal information, hung on October 23–25, 2016, in Thessaloniki, Greece, depicts a fascinating collaborative event of neural networks with monstrous facts and different studying technologies.

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Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece (Advances in Intelligent Systems and Computing)

The booklet bargains a well timed picture of neural community applied sciences as an important portion of immense info analytics structures. It promotes new advances and study instructions in effective and leading edge algorithmic methods to reading tremendous info (e. g. deep networks, nature-inspired and brain-inspired algorithms); implementations on diverse computing systems (e.

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A description vector is then computed separately for each such block. The process is successively repeated d times, for larger values of m and n, until m = M and n = N during the last iteration. From an implementation point-of-view, this is executed recursively, in a top-down manner, with the image region that is currently being described at each time, subsequently being partitioned into 4 quadrants. These quadrants serve as input blocks to the 4 recursive function calls of the next step. Thus, the total number S of produced description vectors is given by the sum of the first d terms of a geometric progression: S(d) = 1 · 40 + 1 · 41 + · · · + 1 · 4d−1 = (4d − 1)/3 (1) In the end, a set of description vectors over various image regions and for various scales is produced, including one truly global description vector (computed over the entire Vik ).

The mean duration of the videos is about 182 s, or 4542 frames. The fact that ground truth annotation data provided along with the IMPART dataset describe not key-frames pre-selected by users, as in [6] (which would be highly subjective), but obvious activity segment frame boundaries, was exploited to evaluate the proposed framework as objectively as possible. Given the results of each summarization algorithm for each video, the number of extracted keyframes derived from actually different activity segments (hereafter called independent key-frames) can be used as an indication of summarization success.

In: IEEE International Conference on Image Processing (ICIP), pp. 1120–1124 (2015) 2. : Video summarization: a conceptual framework and survey of the state of the art. J. Vis. Commun. Representation 19(2), 121–143 (2008) 3. : A new method for static video summarization using local descriptors and video temporal segmentation. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 226–233. IEEE (2013) 4. : A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern.

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