Title: 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.