Data Set Information:
For Each feature, a 64 element vector is given per sample of leaf. These vectors are taken as a contigous descriptors (for shape) or histograms (for texture and margin).
For Each feature, a 64 element vector is given per sample of leaf. One file for each 64-element feature vectors. Each row begins with the class label. The remaining 64 elements is the feature vector.
This is a new data set, provisional paper: 'Plant Leaf Classification Using
Probabilistic Integration of Shape, Texture and Margin Features' at SPPRA 2013. Authors:
Charles Mallah, James Cope, and James Orwell or Kingston University London.
Previous parts of the data set relate to feature extraction of leaves from:
J. Cope, P. Remagnino, S. Barman, and P. Wilkin.
Plant texture classification using gabor cooccurrences.
Advances in Visual Computing,
pages 669a€“677, 2010.
T. Beghin, J. Cope, P. Remagnino, and S. Barman.
Shape and texture based plant leaf classification. In
Advanced Concepts for Intelligent Vision Systems,
pages 345a€“353. Springer, 2010.
Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.
James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
The colour images are not included in this submission.
The Leaves were collected in the Royal Botanic Gardens, Kew, UK.
email: james.cope '@' kingston.ac.uk
This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Kingston University London.
Donor of database Charles Mallah: charles.mallah '@' kingston.ac.uk; James Cope: james.cope '@' kingston.ac.uk