Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations
Abstract
We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for learning deep representations, extracting hand-crafted features with a number of image processing techniques, training a linear SVM with feature vectors, merging SVM and CNN results, and identifying the species from a dataset of 57 trees. Our classification results show that fusion of deep representations with hand-crafted features leads to the highest accuracy. The proposed algorithm is embedded in a smart-phone application, which is publicly available. Furthermore, our novel dataset comprised of 5408 leaf images is also made public for use of other researchers.
Cite
@article{arxiv.1701.08291,
title = {Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations},
author = {İlke Çuğu and Eren Şener and Çağrı Erciyes and Burak Balcı and Emre Akın and Itır Önal and Ahmet Oğuz Akyüz},
journal= {arXiv preprint arXiv:1701.08291},
year = {2017}
}