By Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
The publication bargains a well timed photo of neural community applied sciences as an important portion of substantial facts analytics systems. It promotes new advances and learn instructions in effective and leading edge algorithmic ways to reading great facts (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on diversified computing systems (e.g. neuromorphic, photos processing devices (GPUs), clouds, clusters); and massive facts analytics functions to resolve real-world difficulties (e.g. climate prediction, transportation, power management). The publication, which experiences at the moment variation of the resorts convention on significant information, hung on October 23–25, 2016, in Thessaloniki, Greece, depicts an engaging collaborative experience of neural networks with enormous information and different studying technologies.
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Extra info for Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
The set of all cluster centroids is called a codebook and c is the codebook size parameter. Each of the Pi vectors in Di is subsequently assigned to the nearest visual word, in terms of Euclidean distance. The number of description vectors assigned to each of the Kc clusters is an entry in a Kc-dimensional vector. This vector is followingly transformed into a histogram by L1 -normalization, in order to produce the ﬁnal Kc-dimensional video frame feature vector di . The histogram construction process is repeated for all Nf values of i.
It was demonstrated using one large-scale dataset and two smaller datasets that the proposed technique can improve the retrieval precision and, at the same time, reduce the storage requirements and the retrieval time by almost two orders of magnitude. There are several future research directions. In this work it was assumed that the face images were already aligned using face detection and alignment techniques and a ﬁxed grid was used for the O-SBoF technique. However, the proposed method can be combined with facial feature detectors to more accurately deﬁne the regions used for feature extraction and further improve the retrieval precision and reduce the representation size.
Energy and entropy. Moreover, on top of the horizontal and the vertical proﬁle vector, a third block vector is constructed by vectorizing the actual block in row-major order. The same statistical synopsis is also applied on this vector, resulting in an 18-dimensional block description vector fi = [m1H , · · · , m6H , m1V , · · · , m6V , m1B , · · · , m6B ]T . Using this notation, H, V and B refer to the extracted statistical properties of horizontal proﬁle vectors, vertical proﬁle vectors and block vectors, respectively.
Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece by Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco