Comparative Study of Feature Extraction Components from Several Wavelet Transformations for Ornamental Plants

Human has a duty to preserve the nature, preserving the plant is one of the examples. This research emphasis on ornamental plant that has functionality not only as ornament plant but also as a medicinal plant. Purpose of this research is to find the best of the particular feature extraction components from several wavelet transformations. It consists of Daubechies, Dyadic, and Dual-tree complex wavelet transformation. Dyadic and Dual-tree complex wavelet transformations have shift invariant property. While Daubechies is a standard wavelet transform that widely used for many applications. This comparison is utilizing leaf image datasets from ornamental plants. From the experiments, obtained that best classification performance attained by Dual-tree complex wavelet transformation with 96.66% of overall performance result.

@article{Arai2014,
  title     = {Comparative Study of Feature Extraction Components from Several Wavelet Transformations for Ornamental Plants},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {3},
  number    = {2},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Indra Nugraha Abdullah and Hiroshi Okumura},
  doi       = {10.14569/IJARAI.2014.030202},
  url       = {https://doi.org/10.14569/IJARAI.2014.030202}
}