Plant identification: Two dimensional-based vs. One dimensional-based feature extraction methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096577" target="_blank" >RIV/61989100:27240/15:86096577 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-19719-7_33" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19719-7_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-19719-7_33" target="_blank" >10.1007/978-3-319-19719-7_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Plant identification: Two dimensional-based vs. One dimensional-based feature extraction methods

Popis výsledku v původním jazyce

In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-basedmethod, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner, was used. The proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, wasmuch better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a c

Název v anglickém jazyce

Plant identification: Two dimensional-based vs. One dimensional-based feature extraction methods

Popis výsledku anglicky

In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-basedmethod, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner, was used. The proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, wasmuch better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a c

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Soft Computing. Volume 368

ISBN

978-3-319-19718-0

ISSN

1615-3871

e-ISSN

—

Počet stran výsledku

11

Strana od-do

375-385

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Burgos

Datum konání akce

15. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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