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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 82 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Voltage Control of Distribution Grids with Multi-Microgrids Using Reactive Power Management, WLODARCZYK, P., SUMPER, A., CRUZ, M.
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3D Object Metamorphosis with Pseudo Metameshes, MOCANU, B., TAPU, R., ZAHARIA, T.
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Spatiotemporal Data Mining for Distribution Load Expansion, ARANGO, H. G., LAMBERT-TORRES, G., de MORAES, C. H. V., BORGES DA SILVA, L. E.
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New Boost-Type PFC MF-Vienna PWM Rectifiers with Multiplied Switching Frequency, FLORICAU, D., TUDORACHE, T., KREINDLER, L.
Issue 4/2015



Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  4/2015 - 6

A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video Surveillance

MIRONICA, I. See more information about MIRONICA, I. on SCOPUS See more information about MIRONICA, I. on IEEExplore See more information about MIRONICA, I. on Web of Science, MITREA, C. A. See more information about  MITREA, C. A. on SCOPUS See more information about  MITREA, C. A. on SCOPUS See more information about MITREA, C. A. on Web of Science, IONESCU, B. See more information about  IONESCU, B. on SCOPUS See more information about  IONESCU, B. on SCOPUS See more information about IONESCU, B. on Web of Science, LAMBERT, P. See more information about LAMBERT, P. on SCOPUS See more information about LAMBERT, P. on SCOPUS See more information about LAMBERT, P. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,590 KB) | Citation | Downloads: 298 | Views: 1,430

Author keywords
automated video surveillance, Fisher kernel representation, multiple-instance object retrieval

References keywords
recognition(11), video(10), vision(9), surveillance(9), image(9), processing(7), pattern(7), machine(7), classification(7), fisher(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 43 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04006
Web of Science Accession Number: 000368499800006
SCOPUS ID: 84949964857

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This paper presents an automated surveillance system that exploits the Fisher Kernel representation in the context of multiple-instance object retrieval task. The proposed algorithm has the main purpose of tracking a list of persons in several video sources, using only few training examples. In the first step, the Fisher Kernel representation describes a set of features as the derivative with respect to the log-likelihood of the generative probability distribution that models the feature distribution. Then, we learn the generative probability distribution over all features extracted from a reduced set of relevant frames. The proposed approach shows significant improvements and we demonstrate that Fisher kernels are well suited for this task. We demonstrate the generality of our approach in terms of features by conducting an extensive evaluation with a broad range of keypoints features. Also, we evaluate our method on two standard video surveillance datasets attaining superior results comparing to state-of-the-art object recognition algorithms.

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[1] End to end very deep person re-identification, Stefan, Liviu-Daniel, Mironica, Ionut, Mitrea, Catalin Alexandru, Ionescu, Bogdan, 2017 International Symposium on Signals, Circuits and Systems (ISSCS), ISBN 978-1-5386-0674-2, 2017.
Digital Object Identifier: 10.1109/ISSCS.2017.8034923

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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