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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Plant identification: Two dimensional-based vs. One dimensional-based feature extraction methods
Original language description
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
Czech name
—
Czech description
—

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)