Merge branch 'master' of skozl.com:e4-vision

1 files changed, 7 insertions, 7 deletions

diff --git a/report/paper.md b/report/paper.md
index 439ab37..e2b2aac 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -15,7 +15,7 @@ The number of clusters or the number of centroids determines the vocabulary size
 
 ## Bag-of-words histogram quantisation of descriptor vectors
 
-An example histograms for training and testing images is shown on figure \ref{fig:histo_tr}, computed with a vocubulary size of 100. 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}. A small vocabulary size turns out to misrepresent the information contained in the different patches, resulting in poor classification accuracy. When the vocabulary size gets too big (too many k-mean centroids), the result is instead overfitting. Figure \ref{fig:km_vocsize} shows a plateau after 60 cluster centers.
+An example histograms for training and testing images is shown on figure \ref{fig:histo_tr}, computed with a vocubulary size of 100. 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-means centroids) on the classificaiton accuracy in figure \ref{fig:km_vocsize}. A small vocabulary size turns out to misrepresent the information contained in the different patches, resulting in poor classification accuracy. When the vocabulary size gets too big (too many k-mean centroids), the result is instead overfitting. Figure \ref{fig:km_vocsize} shows a plateau after 60 cluster centers.
 
 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 we tried is applying PCA to the descriptors vecotrs to improve time performance. However in this case the descriptors' size is relatively small, and for such reason we opted to avoid PCA for further training. 
 
@@ -39,7 +39,7 @@ this coursework, only leading to an increase in execution time.
 ## Hyperparameters tuning
 
 Figure \ref{fig:km-tree-param} shows the effect of tree depth and number of trees
-for kmean 100 cluster centers.
+for K-means 100 cluster centers.
 
 \begin{figure}[H]
 \begin{center}
@@ -50,7 +50,7 @@ for kmean 100 cluster centers.
 \end{center}
 \end{figure}
 
-Figure \ref{fig:kmeanrandom} shows randomness parameter for kmean 100.
+Figure \ref{fig:kmeanrandom} shows randomness parameter for K-means 100.
 
 \begin{figure}[H]
 \begin{center}
@@ -70,7 +70,7 @@ more. This is due to the complexity added by the two-pixels test, since it adds
 \begin{figure}[H]
 \begin{center}
 \includegraphics[width=18em]{fig/2pixels_kmean.pdf}
-\caption{Kmean classification accuracy changing the type of weak learners}
+\caption{K-means classification accuracy changing the type of weak learners}
 \label{fig:2pt}
 \end{center}
 \end{figure}
@@ -91,7 +91,7 @@ more. This is due to the complexity added by the two-pixels test, since it adds
 \begin{figure}[H]
 \begin{center}
 \includegraphics[width=18em]{fig/e100k256d5_cm.pdf}
-\caption{e100k256d5cm Kmean Confusion Matrix}
+\caption{e100k256d5cm K-means Confusion Matrix}
 \label{fig:km_cm}
 \end{center}
 \end{figure}
@@ -100,14 +100,14 @@ more. This is due to the complexity added by the two-pixels test, since it adds
 \begin{center}
 \includegraphics[width=10em]{fig/success_km.pdf}
 \includegraphics[width=10em]{fig/fail_km.pdf}
-\caption{Kmean: Success on the left; Failure on the right}
+\caption{K-means: Success on the left; Failure on the right}
 \label{fig:km_succ}
 \end{center}
 \end{figure}
 
 # RF codebook
 
-An alternative to codebook creation via *K-means* involves using an ensemble of totally random trees. We code each decriptor according to which leaf of each tree in the ensemble it is sorted. This effectively performs and unsupervised transformation of our dataset to a high-dimensional sparse representation. The dimension of the vocubulary size is determined by the number of leaves in each random tree and the ensemble size.
+An alternative to codebook creation via K-means involves using an ensemble of totally random trees. We code each decriptor according to which leaf of each tree in the ensemble it is sorted. This effectively performs and unsupervised transformation of our dataset to a high-dimensional spare representation. The dimension of the vocubulary size is determined by the number of leaves in each random tree and the ensemble size.
 
 \begin{figure}[H]
 \begin{center}