Detail publikace

SIFT and SURF based feature extraction for the anomaly detection

BILÍK, Š. HORÁK, K.

Originální název

SIFT and SURF based feature extraction for the anomaly detection

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this paper, we suggest a way to use SIFT and SURF algorithms to extract the image features for anomaly detection. We use those feature vectors to train various classifiers on a real-world dataset in the semi-supervised (with a small number of faulty samples) manner with a large number of classifiers and in the one-class (with no faulty samples) manner using the SVDD and SVM classifier. We prove, that the SIFT and SURF algorithms could be used as feature extractors, that they could be used to train a semi-supervised and one-class classifier with an accuracy around 89\% and that the performance of the one-class classifier could be comparable to the semi-supervised one. We also made our dataset and source code publicly available.

Klíčová slova

Anomaly detection;Object descriptors;Machine Learning;SIFT;SURF

Autoři

BILÍK, Š.; HORÁK, K.

Vydáno

26. 4. 2022

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6029-4

Kniha

Proceedings I of the 28 th Conference STUDENT EEICT 2022 General Papers

Edice

1

Strany od

459

Strany do

464

Strany počet

6

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf

BibTex

@inproceedings{BUT177722,
  author="Šimon {Bilík} and Karel {Horák}",
  title="SIFT and SURF based feature extraction for the anomaly detection",
  booktitle="Proceedings I of the 28 th Conference STUDENT EEICT 2022 General Papers",
  year="2022",
  series="1",
  pages="459--464",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf"
}