Update README.md

README.md

@@ -8,11 +8,14 @@ tags:
 size_categories:
 - 1M<n<10M
 ---
-Description: One-hundred plant species leaves dataset (Class = Shape).
+Description:
+
+One-hundred plant species leaves dataset (Class = Shape).
 
 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.
 
 Sources: 
+
 (a) Original owners of colour Leaves Samples:
  James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.  
  The colour images are not included.  
@@ -20,17 +23,27 @@ Sources:
  email: james.cope@kingston.ac.uk  
  
 (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.  
- Donor of the database:  Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.  
+
+ Donor of the database:  
+ 
+ Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.  
  
  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:
  
 ‘data_Sha_64.txt’ -> prediction based on shape
+
 ‘data_Tex_64.txt’ -> prediction based on texture
+
 ‘data_Mar_64.txt’ -> prediction based on margin
+
 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.
 
 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.