Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and Vein Features

TL;DR

This paper presents a plant identification method combining Polar Fourier Transform, color moments, and vein features, achieving high accuracy in foliage plant retrieval using the Flavia dataset.

Contribution

It introduces a novel combination of features for foliage plant retrieval that outperforms existing methods like PNN, SVM, and Fourier Transform.

Findings

Achieved 90.80% accuracy on 50 plant species.

Outperformed PNN, SVM, and Fourier Transform methods.

Effective for foliage plants with diverse colors and patterns.

Abstract

This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants.