How Far Can You Get By Combining Change Detection Algorithms?

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Misc{oai:arXiv.org:1505.02921,
  author =       "Simone Bianco and Gianluigi Ciocca and 
                 Raimondo Schettini",
  title =        "How Far Can You Get By Combining Change Detection
                 Algorithms?",
  year =         "2015",
  month =        may # "~12",
  note =         "Comment: Submitted to IEEE Transactions on Image
                 Processing",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1505.02921",
  keywords =     "genetic algorithms, genetic programming, computer
                 science - computer vision and pattern recognition",
  URL =          "http://arxiv.org/abs/1505.02921",
  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.",
  notes =        "see \cite{Bianco:ieeeTEC}",
}

Genetic Programming entries for Simone Bianco Gianluigi Ciocca Raimondo Schettini

Citations