Open-set plant identification using an ensemble of deep convolutional neural networks

Open-set recognition, a challenging problem in computer vision, is concerned with identification or verification tasks where queries may belong to unknown classes. This work describes a fine-grained plant identification system consisting of an ensemble of deep convolutional neural networks within an open-set identification framework. Two wellknown deep learning architectures of VGGNet and GoogLeNet, pretrained on the object recognition dataset of ILSVRC 2012, are finetuned using the plant dataset of LifeCLEF 2015. Moreover, GoogLeNet is fine-tuned using plant and non-plant images for rejecting samples from non-plant classes. Our systems have been evaluated on the test dataset of PlantCLEF 2016 by the campaign organizers and our best proposed model has achieved an official score of 0.738 in terms of the mean average precision, while the best official score is 0.742

Guiding Active Contours for Tree Leaf Segmentation and Identification

International audienceIn the process of tree identi cation from pictures of leaves in a natural background, retrieving an accurate contour is a challenging and crucial issue. In this paper we introduce a method designed to deal with the obstacles raised by such complex images, for simple and lobed tree leaves. A rst segmentation step based on a light polygonal leaf model is first performed, and later used to guide the evolution of an active contour. Combining global shape descriptors given by the polygonal model with local curvature-based features, the leaves are then classi ed over nearly 50 tree species

Advanced shape context for plant species identification using leaf image retrieval

International audienceThis paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach

Identification d'espèces végétales par une description géométrique locale d'images de feuilles

Plant species identification, usually performed by specialists, is based on the observation of its organs and mostly on visual criteria. Thanks to its ease of acquisition, the leaf is the most used organ. In addition, it contains important information on the taxonomy of the plant. This enables the use of computer vision and pattern recognition techniques for developing an automatic recognition process of the plant species from a leaf image. We introduce a new approach to identify plant species, based on the description of the following leaf characteristics : its shape, its salient points and its venation. First, the shape of the leaf is represented by local descriptors associated to a set of points sampled on the contour. Different multi-scale triangular representations are introduced and compared. To describe the salient points of the leaf, we propose a shape context based representation. Finally, the venation is extracted by detecting elementary linear structures with morphological tools. The venation network is described by its main directions and its spatial distribution in the context of the leaf boundary. A local matching method is used for all descriptors. Evaluations, conducted on six publicly available plant identification benchmarks, show that our approaches identify the plant species of the leaf in most of the cases and that the late fusion of the proposed descriptors improves the identification process.Il est nécessaire de reconnaître les espèces végétales afin de préserver la biodiversité des écosystèmes. L’identification d’une plante, habituellement effectuée par les experts, se base sur l’observation de ses organes et en majeure partie sur des critères visuels. La feuille est l’organe le plus utilisé grâce à sa facilité d’acquisition. De plus, celle-ci contient une information importante sur la taxonomie de la plante. Ceci permet d’envisager d’utiliser l’analyse d’images pour élaborer un processus de reconnaissance automatique de l’espèce végétale à partir de la donnée d’une image de feuille. Nous introduisons une nouvelle approche d’identification d’espèces végétales, basée sur la description des caractères foliaires suivants : la forme, les points saillants et la nervation. En premier lieu, la forme de la feuille est représentée par des descripteurs locaux associés aux points échantillonnés sur le contour. Différentes représentations triangulaires multi-échelle sont introduites et comparées. Pour décrire les points saillants de la feuille, nous proposons une représentation dérivée du contexte de forme (Shape Context). Finalement, la nervation est extraite par la détection de structures linéaires élémentaires avec des outils morphologiques. Le réseau de nervures extrait est décrit par ses directions principales et sa répartition spatiale dans le contexte de la surface de la feuille.Pour tous les descripteurs, une méthode de mise en correspondance locale est utilisée. Des évaluations, menées sur six bases de feuilles publiques, montrent que nos approches permettent généralement d’identifier l’espèce végétale de la feuille et que la fusion tardivedes descripteurs augmente la précision de l’identification

The CLEF 2011 Plant Images Classification Task

International audienceImageCLEF’ plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of the plant identification task at ImageCLEF 2011, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation result

The CLEF 2011 Plant Images Classification Task

International audienceImageCLEF’ plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of the plant identification task at ImageCLEF 2011, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation result

The CLEF 2011 Plant Images Classification Task

International audienceImageCLEF’ plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. This paper presents an overview of the resources and assessments of the plant identification task at ImageCLEF 2011, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation result