Datasets:

yuqingXing
/

leave_data

---
task_categories:
- image-classification
language:
- en
tags:
- biology
size_categories:
- 10M<n<100M
---
One-hundred plant species leaves dataset. The dataset is derived from this paper: 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.

(1)Sources: 

 (a) Original owners of colour Leaves Samples:
 James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.  
 The colour images are not included.  
 The Leaves were collected in the Royal Botanic Gardens, Kew, UK.  
 email: james.cope@kingston.ac.uk  
 
 (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.

 
 (2)Donor of the database:  
  Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.  
 
 
(3)Dataset Information: 
 
 The original data directory contains the binary images (masks) of the leaf samples (colour images not included). There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). So, there are three different files, one for each feature problem. 
 Each row has a 64-element feature vector followed by the Class label. 
 There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.

‘data_Sha_64.txt’ -> prediction based on shape

‘data_Tex_64.txt’ -> prediction based on texture

‘data_Mar_64.txt’ -> prediction based on margin

 

(4)References:

[1]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.

[2]J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor co-occurrences. Advances in Visual Computing, pages 699-677, 2010.

[3]T. Beghin, J. Cope, P. Remagnino, and S. Barman. Shape and texture based plant leaf classification. In: Advanced Concepts for Intelligent Vision Systems, pages 345-353. Springer, 2010.

	---
	task_categories:
	- image-classification
	language:
	- en
	tags:
	- biology
	size_categories:
	- 10M<n<100M
	---
	One-hundred plant species leaves dataset. The dataset is derived from this paper: 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.

	(1)Sources:

	(a) Original owners of colour Leaves Samples:
	James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
	The colour images are not included.
	The Leaves were collected in the Royal Botanic Gardens, Kew, UK.
	email: james.cope@kingston.ac.uk

	(b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.


	(2)Donor of the database:
	Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.


	(3)Dataset Information:

	The original data directory contains the binary images (masks) of the leaf samples (colour images not included). There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). So, there are three different files, one for each feature problem.
	Each row has a 64-element feature vector followed by the Class label.
	There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.

	‘data_Sha_64.txt’ -> prediction based on shape

	‘data_Tex_64.txt’ -> prediction based on texture

	‘data_Mar_64.txt’ -> prediction based on margin



	(4)References:

	[1]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.

	[2]J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor co-occurrences. Advances in Visual Computing, pages 699-677, 2010.

	[3]T. Beghin, J. Cope, P. Remagnino, and S. Barman. Shape and texture based plant leaf classification. In: Advanced Concepts for Intelligent Vision Systems, pages 345-353. Springer, 2010.