Participation of INRIA & Pl@ntNet to ImageCLEF 2011 plant images classification task

International audienceThis paper presents the participation of INRIA IMEDIA group and the Pl@ntNet project to ImageCLEF 2011 plant identification task. ImageCLEF's 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. IMEDIA submitted two runs to this task and obtained the best evaluation score for two of the three image categories addressed within the benchmark. The paper presents the two approaches employed, and provides an analysis of the obtained evaluation results

PlantNet Participation at LifeCLEF2014 Plant Identification Task

International audienceThis paper describes the participation of Inria within the Pl@ntNet project7 at the LifeCLEF2014 plant identication task. The aim of the task was to produce a list of relevant species for each plant observation in a test dataset according to a training dataset. Each plant observation contains several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). Our system treated independently each category of organ/view and then a late hierarchical fusion is used in order to combine the results on visual content analysis from the most local level analysis in pictures to the highest level related to a plant observation. For the photographs of flowers, leaves, fruits, stems, branches and entire views of plants, a large scale matching approach of local features extracted using different spatial constraints is used. For scans, the method combines the large scale matching approach with shape descriptors and geometric parameters on shape boundary. Then, several fusion methods are experimented through the four submitted runs in order to combine hierarchically the local responses to the final response at the plant observation level. The four submitted runs obtained good results and got the 4th to the 7th place over 27 submitted runs by 10 participating team

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

Crowdsourcing Biodiversity Monitoring: How Sharing your Photo Stream can Sustain our Planet

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A look inside the Pl@ntNet experience

International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps

Plant species recognition using spatial correlation between the leaf margin and the leaf salient points.

International audienceIn this paper, we propose an automatic approach for plant species identification, based on the visual information provided by the plant leaves. More precisely, we consider two sources of information: the leaf margin and the leaf salient points. We investigate two shape context based descriptors: the first one describes the leaf boundary while the second descriptor represents the spatial correlation between salient points of the leaf and its margin. We also study the performance of the fusion of these two descriptors on the ImageCLEF 2011 and 2012 leaf datasets. Experiments show the effectiveness and the efficiency of the proposed method

LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

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

The ImageCLEF 2012 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of plant identification, more precisely on the 126 tree species identification based on leaf images. Three types of image content are considered: Scan, Scan-like (leaf photographs with a white uniform background), and Photograph (unconstrained leaf with natural background). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of eleven groups from eight countries and with a total of 30 runs submitted, involving distinct and original methods, this second year pilot task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification