Abstract
Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification.
Highlights
Flowering plants play a key role in terrestrial ecosystems, humans increasingly lack the ability for their classification [1]
We compared classification accuracy achieved using Hessian, HessLap, Harris, and HarrLap with and without affine shape estimation. Evaluating these results, we reduced the list of feature detectors and evaluated their classification accuracies in combination with shape descriptors, i.e., Scale-Invariant Feature Transform (SIFT), SURF, and Histogram of Oriented Gradients (HOG), as well as color descriptors, i.e., Robust Hue Histograms (RHH), Opponent Angle Histograms (OppA), and Discriminant Color Descriptor (DCD)
We performed a comprehensive comparison of state-of-the-art methods within an image classification pipeline for flower image based plant species classification using local features
Summary
Flowering plants play a key role in terrestrial ecosystems, humans increasingly lack the ability for their classification [1]. The classical way of plant classification, i.e., following a single access identification tree of dichotomous keys, is a complicated and tedious. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.