Invariant Feature Descriptor based on Harmonic Image Transform for Plant Leaf Retrieval

ABSTRACT
Feature descriptor for image retrieval has emerged as an important part of computer vision and image
analysis application. In the last decades, researchers have used algorithms to generate effective, efficient
and steady methods in image processing, particularly shape representation, matching and leaf retrieval.
Existing leaf retrieval methods are insufficient to achieve an adequate retrieval rate due to the inherent
difficulties related to available shape descriptors of different leaf images. Shape analysis and comparison
for plant leaf retrieval are investigated in this study. Different image features may result in different
significance interpretation of images, even though they come from almost similarly shaped of images.
A new image transform, known as harmonic mean projection transform (HMPT), is proposed in this
study as a feature descriptor method to extract leaf features. By using harmonic mean function, the
signal carries information of greater importance is considered in signal acquisition. The selected image
is extracted from the whole region where all the pixels are considered to get a set of features. Results
indicate better classification rates when compared with other classification methods.