Publication result detail

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

MUSIL, M.

Original Title

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

English Title

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

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

Support Vector Machine (SVM) is a technique forclassification and regression. It uses a decision surface called hyperplanethat depends on the regularization parameter and training points lying in themargin of the hyperplane. The run-time complexity of SVM may be reduced throughthe hyperplane affected by the regularization parameter. We deal with rails recognition in images taken fromthe camera mounted on the board of the locomotive. For the purpose of railcandidates detection, we deployed an algorithm using SVM. We performed several experimentsunder different settings. In this paper, we introduce an algorithm using SVMand the impact of its regulation parameter as well as others possible onSVM-performance. The main goal is to decrease time-complexity while maintainingclassification success rate.

English abstract

Support Vector Machine (SVM) is a technique forclassification and regression. It uses a decision surface called hyperplanethat depends on the regularization parameter and training points lying in themargin of the hyperplane. The run-time complexity of SVM may be reduced throughthe hyperplane affected by the regularization parameter. We deal with rails recognition in images taken fromthe camera mounted on the board of the locomotive. For the purpose of railcandidates detection, we deployed an algorithm using SVM. We performed several experimentsunder different settings. In this paper, we introduce an algorithm using SVMand the impact of its regulation parameter as well as others possible onSVM-performance. The main goal is to decrease time-complexity while maintainingclassification success rate.

Keywords

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

Key words in English

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

Authors

MUSIL, M.

RIV year

2016

Released

28.12.2015

Publisher

Akademické sdružení MAGNANIMITAS Assn.

Location

Hradec Králové

ISBN

978-80-87952-12-2

Book

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

Edition

vol. VI

Pages from

2138

Pages to

2146

Pages count

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"
}