A Model of Plant Identification System Using GLCM, Lacunarity and Shen Features
Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems....
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11803/1/A_Model_of_Plant_Identification_System_Using_GLCM%2C_Lacunarity_and_Shen.pdf http://eprints.utem.edu.my/id/eprint/11803/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Recently, many approaches have been introduced by several researchers to identify plants. Now,
applications of texture, shape, color and vein features are common practices. However, there are many
possibilities of methods can be developed to improve the performance of such identification systems.
Therefore, several experiments had been conducted in this research. As a result, a new novel approach by
using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier
gives a better result compared to other plant identification systems. For comparison, this research used two
kinds of several datasets that were usually used for testing the performance of each plant identification
system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and
95.00% when using the Foliage dataset and outperforms other approaches. |
|---|