Computer Vision Classification of Leaves from Swedish Trees

By Oskar Söderkvist. 2001

330 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017, background): “This dataset is considered very challenging due to its high inter-species similarity [127].”
Highly Comparative Feature-Based Time-Series Classification (2014, unknown): “2.3.”
Is Rotation a Nuisance in Shape Recognition? (2014, methodology): “Examples of the Swedish leaf dataset ([14]).”
CENTRIST: A Visual Descriptor for Scene Categorization (2011, methodology): “12% Söderkvist [37] Contour only 82.”
Hierarchical Matching of Deformable Shapes (2007, methodology): “Similar to the methods in [26] and [16], we randomly select 25 training images from each species and classify the remaining images using a nearest neighbor approach.”
Automated plant species identification—Trends and future directions (2018, background): “Swedish leaf [30] 15 1,125 scan plain leaves trees”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, methodology): “Following the standard protocol [37], [58], we randomly select 25 images per class for training and the rest for testing.”
Shape Classification Using the Inner-Distance (2007, methodology): “The Swedish leaf data set comes from a leaf classification project at Linköping University and the Swedish Museum of Natural History [42].”