Combining Leaf Salient Points and Leaf Contour Descriptions for Plant Species Recognition

International audienceManual Plant identification done by experts is tedious and time consuming. This process needs to be automatic and easy to handle by the different stakeholders. In this paper, we propose an original method for plant species recognition, based on the leaf observation. We consider two sources of information: the leaf margin and the leaf salient points. For the leaf shape description, we investigate the shape context descriptor and two multiscale triangular approaches: the well-known triangle area representation (TAR) and the triangle side length representation (TSL). We propose then their combination with a shape-context based descriptor that represents the spatial correlation between the leaf salient points and the leaf margin. Experiments are carried out on three public leaf datasets. Results show that our approach achieves a high retrieval accuracy and outperforms state-of-art methods

Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

International audienceWith the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia

An expert botanical feature extraction technique based on phenetic features for identifying plant species

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost

Inria's participation at ImageCLEF 2013 Plant Identification Task

International audienceThis paper describes the participation of Inria within the Pl@ntNet project at ImageCLEF2013 plant identification task. For the SheetAsBackground category (scans or photographs of leaves with a uniform background), the submitted runs used a multiscale triangle-based approaches, either alone or combined with other shape-based descriptors. For the NaturalBackground category (unconstrained photographs of leaves, flowers, fruits, stems,...), the four submitted runs used local features extracted using different geometric constraints. Three of them were based on large scale matching of individual local feature, while the last one used a Fisher vector representation. Metadata like the flowering date or/and plant identifier were successfully combined to the visual content. Overall the proposed methods performed very well for all categories and sub-categories

A shape-based approach for leaf classification using multiscale triangular representation

International audienceIn this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods

A New Leaf Venation Detection Technique for Plant Species Classification

This paper presents a novel approach to classify the leaf shape and to identify plant species using venation detection. The proposed approach consists of five main steps to extract the leaf venation including canny edge detection, remove leaf boundary, extract curve, and produce hue normalization image and image fusion. Moreover, to localize the edge direction efficiently, the lines that extracted from pre-processing, are further divided into smaller segments. Thirty-two leaf images of Malaysian plants are analysed and evaluated with two different datasets, Flavia and Acer. The best accuracy is obtained by 99.3% and 91.06% for Flavia and Acer datasets respectively. Experimental results show the effectiveness of the proposed approach for shape recognition with high accuracy. Keywords: Leaf Venation; plant species; features extraction; features selection; classification

Efficient Image Segmentation and Segment-Based Analysis in Computer Vision Applications

This dissertation focuses on efficient image segmentation and segment-based object recognition in computer vision applications. Special attention is devoted to analyzing shape, of particular importance for our two applications: plant species identification from leaf photos, and object classification in remote sensing images. Additionally, both problems are bound by efficiency, constraining the choice of applicable methods: leaf recognition results are to be used within an interactive system, while remote sensing image analysis must scale well over very large image sets. Leafsnap was the first mobile app to provide automatic recognition of tree species, currently counting with over 1.7 million downloads. We present an overview of the mobile app and corresponding back end recognition system, as well as a preliminary analysis of user-submitted data. More than 1.7 million valid leaf photos have been uploaded by users, 1.3 million of which are GPS-tagged. We then focus on the problem of segmenting photos of leaves taken against plain light-colored backgrounds. These types of photos are used in practice within Leafsnap for tree species recognition. A good segmentation is essential in order to make use of the distinctive shape of leaves for recognition. We present a comparative experimental evaluation of several segmentation methods, including quantitative and qualitative results. We then introduce a custom-tailored leaf segmentation method that shows superior performance while maintaining computational efficiency. The other contribution of this work is a set of attributes for analysis of image segments. The set of attributes is designed for use in knowledge-based systems, so they are selected to be intuitive and easily describable. The attributes can also be computed efficiently, to allow applicability across different problems. We experiment with several descriptive measures from the literature and encounter certain limitations, leading us to introduce new attribute formulations and more efficient computational methods. Finally, we experiment with the attribute set on our two applications: plant species identification from leaf photos and object recognition in remote sensing images

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

Cataloging Public Objects Using Aerial and Street-Level Images – Urban Trees

Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-of-the-art CNN-based object detectors and classifiers. We test our method on “Pasadena Urban Trees”, a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance