Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Leafsnap: a computer vision system for automatic plant species identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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To automatically identify plant species is very useful for ecologists, amateur botanists, educators, and so on. In this paper, an Android-based mobile application designed to automatically identify plant species by the photographs of tree leaves is described. In this application, one leaf image can be either a digital image from the existing leaf image database or a picture collected by a camera. The picture should be a single leaf placed on a light and untextured background without other clutter. The identification process consists of totally three steps: leaf image segmentation, feature extraction, and species identification. The demo system is evaluated on the ImageCLEF2012 Plant Identification database which contains 126 tree species from the French Mediterranean area. The output of the system to users is the top several species which match the query leaf image the best, as well as the textual descriptions and additional images about leaves, flowers, etc., of theirs. Our system works well with state-of-the-art identification performance.