Typo fixes

1 files changed, 2 insertions, 2 deletions

diff --git a/report/paper.md b/report/paper.md
index ac72f2b..1b03992 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -15,9 +15,9 @@ The number of clusters or the number of centroids determine the vocabulary size
 
 ## Bag-of-words histogram quantisation of descriptor vectors
 
-An example histogram for training image shown on figure {fig:histo_tr}, computed with a vocubulary size of 100. A corresponding testing image of the same class is shown in figure \ref{fig:histo_te}. The histograms appear to have similar counts for the same words, demonstrating they had a descriptors which matched the *keywowrds* in similar proportions. We later look at the effect of the vocubalary size (as determined by the number of K-mean centroids) on the classificaiton accuracy in figure \ref{fig:km_vocsize}.
+An example histogram for training image shown on figure \ref{fig:histo_tr}, computed with a vocubulary size of 100. A corresponding testing image of the same class is shown in figure \ref{fig:histo_te}. The histograms appear to have similar counts for the same words, demonstrating they had a descriptors which matched the *keywowrds* in similar proportions. We later look at the effect of the vocubalary size (as determined by the number of K-mean centroids) on the classificaiton accuracy in figure \ref{fig:km_vocsize}.
 
-The time complexity of quantisation with a K-means codebooks is $O(n^{dk+1))$ , where n is the number of entities to be clustered, d is the dimension and k is the cluster count @cite[km-complexity]. As the computation time is high, the tests we use a subsample of descriptors to compute the centroids. An alternative method is NUNZIO PUCCI WRITE HERE
+The time complexity of quantisation with a K-means codebooks is $O(n^{dk+1})$ , where n is the number of entities to be clustered, d is the dimension and k is the cluster count @cite[km-complexity]. As the computation time is high, the tests we use a subsample of descriptors to compute the centroids. An alternative method is NUNZIO PUCCI WRITE HERE
 
 \begin{figure}[H]
 \begin{center}