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-# K-means codebook 
-
-We randomly select 100k descriptors for K-means clustering for building the visual vocabulary
-(due to memory issue). Open the main_guideline.m and select/load the dataset.
-```
-[data_train, data_test] = getData('Caltech');
-```
-Set 'showImg = 0' in getData.m if you want to stop displaying training and testing images.
-Complete getData.m by writing your own lines of code to obtain the visual vocabulary and the
-bag-of-words histograms for both training and testing data. Show, measure and
-discuss the followings: 
+# Codebooks
+
+## K-means codebook 
+
+A common technique for codebook generation involves utilising K-means clustering on a sample of the
+image descriptors. In this way descriptors may be mapped to *visual* words which lend themselves to
+binning and therefore the creation of bag-of-words histograms for the use of classification.
+
+In this courseworok 100-thousand descriptors have been selected to build the visual vocabulary from the
+Caltech dataset.
 
 ## Vocabulary size 
 
+The number of clusters or the number of centroids determine the vocabulary size.
+
+![Bag-of-words Training histogram](fig/km-histogram.pdf)
+![Bag-of-words Testing histogram](fig/km-histtest.pdf)
+
 ## Bag-of-words histograms of example training/testing images
 
 ## Vector quantisation process