SIFT and SURF based feature extraction for the anomaly detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144417" target="_blank" >RIV/00216305:26220/22:PU144417 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

SIFT and SURF based feature extraction for the anomaly detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

SIFT and SURF based feature extraction for the anomaly detection

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů