Detail publikace

Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection

MUSIL, M.

Originální název

Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Support Vector Machine (SVM) is a technique for classification and regression. It uses a decision surface called hyperplane that depends on the regularization parameter and training points lying in the margin of the hyperplane. The run-time complexity of SVM may be reduced through the hyperplane affected by the regularization parameter. We deal with rails recognition in images taken from the camera mounted on the board of the locomotive. For the purpose of rail candidates detection, we deployed an algorithm using SVM. We performed several experiments under different settings. In this paper, we introduce an algorithm using SVM and the impact of its regulation parameter as well as others possible on SVM-performance. The main goal is to decrease time-complexity while maintaining classification success rate.

Klíčová slova

computer-vision, Histogram of Oriented Gradients (HOG), optimization, performance, rail candidates detection, run-time complexity, Support Vector Machine (SVM)

Autoři

MUSIL, M.

Rok RIV

2015

Vydáno

28. 12. 2015

Nakladatel

Akademické sdružení MAGNANIMITAS Assn.

Místo

Hradec Králové

ISBN

978-80-87952-12-2

Kniha

International Masaryk conference for Ph.D. students and young researchers

Edice

vol. VI

Strany od

2138

Strany do

2146

Strany počet

9

URL

http://www.vedeckekonference.cz/index.php?option=com_content&view=article&id=79&Itemid=66&lang=en

BibTex

@inproceedings{BUT123624,
  author="Marek {Musil}",
  title="Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection",
  booktitle="International Masaryk conference for Ph.D. students and young researchers",
  year="2015",
  series="vol. VI",
  pages="2138--2146",
  publisher="Akademické sdružení MAGNANIMITAS Assn.",
  address="Hradec Králové",
  isbn="978-80-87952-12-2",
  url="http://www.vedeckekonference.cz/index.php?option=com_content&view=article&id=79&Itemid=66&lang=en"
}