Rotation invariant wavelet descriptors, a new set of features to enhance plant leaves classification

Abstract Automatic plant leaf recognition can play an important role in plant classification due to leaf’s availability, stable features and good potential to discriminate different kinds of species. Amongst many leaf features like leaf venation, margin, texture and lamina, leaf shape is the most important one due to its better discriminative power and ease of analysis. One of the most common leaf shape descriptors is Elliptic Fourier Descriptor (EFD). In this paper a new shape descriptor is introduced as “Rotation Invariant Wavelet Descriptor” (RIWD). The performance of RIWD is compared with IEFD using Flavia dataset. MLP neural network is used as the classifier in this work. Results analysis shows better performance of the proposed feature in classification accuracy. Furthermore, an optimum feature vector is constructed using a set of textural and morphological features and the RIWD that reached 97.5% classification accuracy with low computational cost in comparison with many reported results in Flavia dataset.

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