Keypoints selection using Evolutionary Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA210268C" target="_blank" >RIV/61988987:17610/20:A210268C - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2718/paper30.pdf" target="_blank" >http://ceur-ws.org/Vol-2718/paper30.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Keypoints selection using Evolutionary Algorithms

Popis výsledku v původním jazyce

This contribution presents the use of neural networks trained by an evolutionary algorithm for a selection of visual keypoints. Visual keypoints play an important role in many computer vision tasks but many algorithms for keypoint detection produce many keypoints which are not useful for the target task. We aim to filter them in a data-driven way. Our model uses a neural network that ranks each keypoint by a relevancy score that we use to choose top-K keypoints with the highest rank. These keypoints are then used for the target task, which is image classification in our case. Because we use discrete operations in our model, we can not easily obtain gradients for weight updates. We, therefore, optimize the weights of the network by CMA-ES algorithm, which enables efficient optimization of continuous parameters of black-box functions. In this article, we present our initial experiments with this method.

Název v anglickém jazyce

Keypoints selection using Evolutionary Algorithms

Popis výsledku anglicky

This contribution presents the use of neural networks trained by an evolutionary algorithm for a selection of visual keypoints. Visual keypoints play an important role in many computer vision tasks but many algorithms for keypoint detection produce many keypoints which are not useful for the target task. We aim to filter them in a data-driven way. Our model uses a neural network that ranks each keypoint by a relevancy score that we use to choose top-K keypoints with the highest rank. These keypoints are then used for the target task, which is image classification in our case. Because we use discrete operations in our model, we can not easily obtain gradients for weight updates. We, therefore, optimize the weights of the network by CMA-ES algorithm, which enables efficient optimization of continuous parameters of black-box functions. In this article, we present our initial experiments with this method.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 20th Conference Information Technologies - Applications and Theory (ITAT 2020)

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

6

Strana od-do

186-191

Název nakladatele

CEUR-WS

Místo vydání

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Místo konání akce

Oravská Lesná, Slovensko

Datum konání akce

18. 9. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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