Combination of Video Change Detection Algorithms by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Bianco:ieeeTEC,
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author = "Simone Bianco and Gianluigi Ciocca and
Raimondo Schettini",
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title = "Combination of Video Change Detection Algorithms by
Genetic Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2017",
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volume = "21",
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number = "6",
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pages = "914--928",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Change
detection, algorithm combining and selection, CDNET",
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ISSN = "1089-778X",
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URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898824",
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DOI = "doi:10.1109/TEVC.2017.2694160",
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size = "15 pages",
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abstract = "Within the field of Computer Vision, change detection
algorithms aim at automatically detecting significant
changes occurring in a scene by analysing the sequence
of frames in a video stream. In this paper we
investigate how state-of-the-art change detection
algorithms can be combined and used to create a more
robust algorithm leveraging their individual
peculiarities. We exploited Genetic Programming (GP) to
automatically select the best algorithms, combine them
in different ways, and perform the most suitable
post-processing operations on the outputs of the
algorithms. In particular, algorithms combination and
post-processing operations are achieved with unary,
binary and n-ary functions embedded into the GP
framework. Using different experimental settings for
combining existing algorithms we obtained different GP
solutions that we termed IUTIS (In Unity There Is
Strength). These solutions are then compared against
state-of-the-art change detection algorithms on the
video sequences and ground truth annotations of the
Change Detection. net (CDNET 2014) challenge. Results
demonstrate that using GP, our solutions are able to
outperform all the considered single state-of-the-art
change detection algorithms, as well as other
combination strategies. The performance of our
algorithm are significantly different from those of the
other state-of-the-art algorithms. This fact is
supported by the statistical significance analysis
conducted with the Friedman Test and Wilcoxon Rank Sum
post-hoc tests.",
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notes = "also known as \cite{7898824} See also
\cite{oai:arXiv.org:1505.02921}",
- }
Genetic Programming entries for
Simone Bianco
Gianluigi Ciocca
Raimondo Schettini
Citations