Towards Multi-Level Classification in Deep Plant Identification

Tesis de Graduación (Doctorado académico en Ingeniería) Instituto Tecnológico de Costa Rica, 2018.In the last decade, automatic identification of organisms based on computer vision techniques has been a hot topic for both biodiversity scientists and machine learning specialists. Early on, plants became particularly attractive as a subject of study for two main reasons. On the one hand, quick and accurate inventories of plants are critical for biodiversity conservation; for example, they are indispensable in conducting ecosystem inventories, defining models for environmental service payments, and tracking populations of invasive plant species, among others. On the other hand, plants are a more tractable group than, for instance, insects. First of all, the number of species is smaller (around 400,000 compared to more than 8 million). Secondly, they are better understood by the scientific community, particularly with respect to their morphometric features. Thirdly, there are large, fast growing databases of digital images of plants generated by both scientists and the general public. Finally, an incremental approach based first on "flat elements" such as leaves and then the whole plant made it feasible to use computer vision techniques early on. As a result, even mobile apps for the general public are available nowadays. This document presents the key results obtained while tackling the general problem of fully automating the identification of plant species based solely on images. It describes the key findings in a research path that started with a restricted scope, namely, identification of plants from Costa Rica by using a morphometric approach that considers images of fresh leaves only. Then, species from other regions of the world were included, but still using hand-crafted feature extractors. A key methodological turn was the subsequent use of Deep Learning techniques on images of any components of a plant. Then we studied and compared the accuracy of a Deep Learning approach to do identifications based on datasets of images of fresh plants and compared it with datasets of herbarium sheet images for the first time. Among the results obtained during this research, potential biases in automatic plant identification dataset were found and characterized. Feasibility of doing transfer learning between different regions of the world was also proven. Even more importantly, it was for the first time demonstrated that herbarium sheets are a good resource to do identifications of plants mounted on herbarium sheets, which provides additional levels of importance to herbaria around the globe. Finally, as a culmination of this research path, this document presents the results of developing a novel multi-level classification approach that uses knowledge about higher taxonomic levels to carry out not only family and genus level identifications but also to try to improve the accuracy of species level identifications. This last step focuses on the creation of a hierarchical loss function based on known plant taxonomies, coupled with multilevel Deep Learning architectures to guide the model optimization with the prior knowledge of a given class hierarchy.En la última década, la identificación automática de organismos basada en técnicas de visión artificial ha sido un tema popular tanto entre los científicos de la biodiversidad como para los especialistas en aprendizaje automático. Al principio, las plantas se volvieron particularmente atractivas como tema de estudio por dos razones principales. Por un lado, los inventarios rápidos y precisos de plantas son críticos para la conservación de la biodiversidad; por ejemplo, son indispensables para realizar inventarios de ecosistemas, definir modelos para pagos de servicios ambientales y rastrear poblaciones de especies de plantas invasoras, entre otros. Por otro lado, las plantas son un grupo más manejable que, por ejemplo, los insectos. En primer lugar, la cantidad de especies es menor (alrededor de 400,000 en comparación con más de 8 millones de insectos). En segundo lugar, la comunidad científica las comprende mejor, en particular con respecto a sus características morfométricas. En tercer lugar, existen grandes bases de datos de imágenes digitales de plantas generadas tanto por científicos como por el público en general. Finalmente, un enfoque incremental basado primero en "elementos planos" como hojas y luego en toda la planta hizo posible el uso de técnicas de visión por computadora desde el principio. Como resultado, incluso las aplicaciones móviles para el público en general están disponibles en la actualidad. Este documento presenta los resultados clave obtenidos mientras se aborda el problema general de automatizar por completo la identificación de especies de plantas basándose únicamente en imágenes. Describe los hallazgos clave en un camino de investigación que comenzó con un alcance restringido, a saber, la identificación de plantas de Costa Rica mediante el uso de un enfoque morfométrico que considera imágenes de hojas frescas solamente. Luego, se incluyeron especies de otras regiones del mundo, pero todavía se utilizaban extractores de características hechos a mano. Un giro metodológico clave fue el uso posterior de técnicas de aprendizaje profundo (deep learning) en imágenes de cualquier componente de una planta. Luego, estudiamos y comparamos la exactitud de un enfoque de aprendizaje profundo para realizar identificaciones basadas en conjuntos de datos de imágenes de plantas frescas y las comparamos con conjuntos de datos de imágenes de hojas de herbario por primera vez. Entre los resultados obtenidos durante esta investigación, se encontraron y caracterizaron posibles sesgos en el conjunto de datos de identificación automática de plantas. La viabilidad de hacer un aprendizaje de transferencia (transfer learning) entre diferentes regiones del mundo también se demostró. Aún más importante, por primera vez se demostró que las láminas de herbario son un buen recurso para hacer identificaciones de plantas montadas sobre láminas de herbario, lo que proporciona niveles adicionales de importancia para herbarios en todo el mundo. Finalmente, como una culminación de este camino de investigación, este documento presenta los resultados del desarrollo de un nuevo enfoque de clasificación multi-nivel (multi-level) que utiliza el conocimiento sobre niveles taxonómicos superiores para llevar a cabo identificaciones a nivel de familia y género, y también para tratar de mejorar la exactitud de identificaciones a nivel de especie. Este último paso se centra en la creación de una función de pérdida jerárquica basada en taxonomías de plantas conocidas, junto con arquitecturas de aprendizaje profundo de niveles múltiples para guiar la optimización del modelo con el conocimiento previo de una jerarquía de clases dada

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on Geoscience and Remote Sensing (TGRS), in press, 201

Large-scale automatic species identification

The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p