How Far Can You Get By Combining Change Detection Algorithms?
Created by W.Langdon from
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- @Misc{oai:arXiv.org:1505.02921,
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author = "Simone Bianco and Gianluigi Ciocca and
Raimondo Schettini",
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title = "How Far Can You Get By Combining Change Detection
Algorithms?",
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year = "2015",
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month = may # "~12",
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note = "Comment: Submitted to IEEE Transactions on Image
Processing",
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bibsource = "OAI-PMH server at export.arxiv.org",
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oai = "oai:arXiv.org:1505.02921",
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keywords = "genetic algorithms, genetic programming, computer
science - computer vision and pattern recognition",
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URL = "http://arxiv.org/abs/1505.02921",
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abstract = "In this paper we investigate if simple change
detection algorithms can be combined and used to create
a more robust change detection algorithm by leveraging
their individual peculiarities. We use Genetic
Programming to combine the outputs (i.e. binary masks)
of the detection algorithms with unary, binary and
n-ary functions performing both masks' combination and
post-processing. Genetic Programming allows us to
automatically select the best algorithms, combine them
in different ways, and perform the most suitable
post-processing operations. Using different
experimental settings, we created two algorithms that
we named IUTIS-1 and IUTIS-2 (In Unity There Is
Strength). These algorithms are compared against
state-of-the-art change detection algorithms on the
video sequences and ground truth annotations of the
ChandeDetection.net (CDNET 2014) challenge. Results
demonstrate that starting from simple algorithms we can
achieve comparable results of more complex
state-of-the-art change detection algorithms, while
keeping the computational complexity affordable for
real-time applications. Moreover, when our framework is
applied to more complex algorithms, the resulting
IUTIS-3 outperforms all the 33 state-of-the-art
algorithms considered.",
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notes = "see \cite{Bianco:ieeeTEC}",
- }
Genetic Programming entries for
Simone Bianco
Gianluigi Ciocca
Raimondo Schettini
Citations