Update README.md

README.md

@@ -8,12 +8,9 @@ tags:
 size_categories:
 - 10M<n<100M
 ---
-Description:
- One-hundred plant species leaves dataset
+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.
 
-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: 
+(1)Sources: 
 
  (a) Original owners of colour Leaves Samples:
  James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.  
@@ -24,11 +21,11 @@ Sources:
  (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.
 
  
- Donor of the database:  
+ (2)Donor of the database:  
   Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.  
  
  
-Dataset Information: 
+(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:
  
@@ -40,7 +37,7 @@ Dataset Information:
 
  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:
+(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.