Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts

Iterative Search with Local Visual Features for Computer Assisted Plant Identification

To support computer assisted plant species identification in a realistic, uncontrolled picture-taking condition, we put forward an approach relying on local image features. It combines query by example and relevance feedback to support both the localization of potentially interesting image regions and the classification of these regions as representing or not the target species. We show that this approach is successful, and makes prior segmentation unnecessary

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

The ImageCLEF 2011 plant images classification task

International audienceImageCLEFs 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 con- text 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 anal- ysis of the main evaluation results

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

Improving Shape Retrieval by Integrating AIR and Modified Mutual k

In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutual kNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutual kNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results

Approche probabiliste hybride pour la recherche d'images par le contenu avec pondération des caractéristiques

Durant la dernière décennie, des quantités énormes de documents visuels (images et vidéos) sont produites chaque jour par les scientifiques, les journalistes, les amateurs, etc. Cette quantité a vite démontré la limite des systèmes de recherche d'images par mots clés, d'où la naissance du paradigme qu'on nomme Système de Recherche d'Images par le Contenu, en anglais Content-Based Image Retrieval (CBIR). Ces systèmes visent à localiser les images similaires à une requête constituée d'une ou plusieurs images, à l'aide des caractéristiques visuelles telles que la couleur, la forme et la texture. Ces caractéristiques sont dites de bas-niveau car elles ne reflètent pas la sémantique de l'image. En d'autres termes deux images sémantiquement différentes peuvent produire des caractéristiques bas-niveau similaires. Un des principaux défis de cette nouvelle vision des systèmes est l'organisation de la collection d'images pour avoir un temps de recherche acceptable. Pour faire face à ce défi, les techniques développées pour l'indexation des bases de données textuelles telles que les arbres sont massivement utilisées. Ces arbres ne sont pas adaptés aux données de grandes dimensions, comme c'est le cas des caractéristiques de bas-niveau des images. Dans ce mémoire, nous nous intéressons à ce défi. Nous introduisons une nouvelle approche probabiliste hybride pour l'organisation des collections d'images. Sur une collection d'images organisée hiérarchiquement en noeuds selon la sémantique des images, nous utilisons une approche générative pour l'estimation des mélanges de probabilités qui représentent l'apparence visuelle de chaque noeud dans la collection. Ensuite nous appliquons une approche discriminative pour l'estimation des poids des caractéristiques visuelles. L'idée dans notre travail, est de limiter la recherche seulement aux noeuds qui représentent mieux la sémantique de la requête, ce qui donne une propriété sémantique à la recherche et diminue le fossé sémantique causé par les caractéristiques de bas-niveau

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Tools for identifying biodiversity: progress and problems

The correct identification of organisms is fundamental not only for the assessment and the conservation of biodiversity, but also in agriculture, forestry, the food and pharmaceutical industries, forensic biology, and in the broad field of formal and informal education at all levels. In this book, the reader will find short presentations of current and upcoming projects (EDIT, KeyToNature, STERNA, Species 2000, Fishbase, BHL, ViBRANT, etc.), plus a large panel of short articles on software, taxonomic applications, use of e-keys in the educational field, and practical applications. Single-access keys are now available on most recent electronic devices; the collaborative and semantic web opens new ways to develop and to share applications; the automatic processing of molecular data and images is now based on validated systems; identification tools appear as an efficient support for environmental education and training; the monitoring of invasive and protected species and the study of climate change require intensive identifications of specimens, which opens new markets for identification research