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Diffstat (limited to 'report')
| -rw-r--r-- | report/paper.md | 53 |
1 files changed, 24 insertions, 29 deletions
diff --git a/report/paper.md b/report/paper.md index 9c7e5bc..57c54f4 100644 --- a/report/paper.md +++ b/report/paper.md @@ -54,7 +54,7 @@ Figure \ref{fig:km-tree-param} shows the effect of tree depth and number of tree \begin{center} \includegraphics[width=12em]{fig/error_depth_kmean100.pdf} \includegraphics[width=12em]{fig/trees_kmean.pdf} -\caption{Classification error varying tree depth (left) and forest size (right)} +\caption{K-means Classification error varying tree depth (left) and forest size (right)} \label{fig:km-tree-param} \end{center} \end{figure} @@ -63,8 +63,9 @@ Random forests will select a random number of features on which to apply a weak \begin{figure}[H] \begin{center} -\includegraphics[width=18em]{fig/new_kmean_random.pdf} -\caption{Classification error for different number of random features} +\includegraphics[width=12em]{fig/new_kmean_random.pdf} +\includegraphics[width=12em]{fig/p3_rand.pdf} +\caption{Classification error for different number of random features; K-means left, RF-codebooks right} \label{fig:kmeanrandom} \end{center} \end{figure} @@ -80,7 +81,7 @@ 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} +\includegraphics[width=14em]{fig/2pixels_kmean.pdf} \caption{K-means classification accuracy changing the type of weak learners} \label{fig:2pt} \end{center} @@ -88,7 +89,7 @@ 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} +\includegraphics[width=14em]{fig/e100k256d5_cm.pdf} \caption{Confusion Matrix: K=256, ClassifierForestSize=100, Depth=5} \label{fig:km_cm} \end{center} @@ -96,8 +97,8 @@ more. This is due to the complexity added by the two-pixels test, since it adds \begin{figure}[H] \begin{center} -\includegraphics[width=10em]{fig/success_km.pdf} -\includegraphics[width=10em]{fig/fail_km.pdf} +\includegraphics[width=8em]{fig/success_km.pdf} +\includegraphics[width=8em]{fig/fail_km.pdf} \caption{K-means + RF Classifier: Success (left); Failure (right)} \label{fig:km_succ} \end{center} @@ -110,7 +111,7 @@ which is $O(\sqrt{D} N \log K)$ compared to $O(DNK)$ for K-means. Codebook mappi \begin{figure}[H] \begin{center} -\includegraphics[width=18em]{fig/256t1_e200D5_cm.pdf} +\includegraphics[width=14em]{fig/256t1_e200D5_cm.pdf} \caption{Confusion Matrix: CodeBookForestSize=256; ClassifierForestSize=200; Depth=5} \label{fig:p3_cm} \end{center} @@ -118,9 +119,9 @@ which is $O(\sqrt{D} N \log K)$ compared to $O(DNK)$ for K-means. Codebook mappi \begin{figure}[H] \begin{center} -\includegraphics[width=10em]{fig/success_3.pdf} -\includegraphics[width=10em]{fig/fail_3.pdf} -\caption{Part3: Success (left) and Failure (right)} +\includegraphics[width=8em]{fig/success_3.pdf} +\includegraphics[width=8em]{fig/fail_3.pdf} +\caption{RF Codebooks + RF Classifier: Success (left); Failure (right)} \label{fig:p3_succ} \end{center} \end{figure} @@ -129,36 +130,20 @@ which is $O(\sqrt{D} N \log K)$ compared to $O(DNK)$ for K-means. Codebook mappi \begin{center} \includegraphics[width=12em]{fig/error_depth_p3.pdf} \includegraphics[width=12em]{fig/trees_p3.pdf} -\caption{Classification error varying trees depth (left) and numbers of trees (right)} +\caption{RF-codebooks Classification error varying trees depth (left) and numbers of trees (right)} \label{fig:p3_trees} \end{center} \end{figure} \begin{figure}[H] \begin{center} -\includegraphics[width=18em]{fig/p3_rand.pdf} -\caption{Effect of randomness parameter on classification error} -\label{fig:p3_rand} -\end{center} -\end{figure} - -\begin{figure}[H] -\begin{center} \includegraphics[width=12em]{fig/p3_vocsize.pdf} \includegraphics[width=12em]{fig/p3_time.pdf} -\caption{Effect of vocabulary size; classification error (left) and time (right)} +\caption{RF-codebooks Effect of vocabulary size; classification error (left) and time (right)} \label{fig:p3_voc} \end{center} \end{figure} -\begin{figure}[H] -\begin{center} -\includegraphics[width=18em]{fig/p3_colormap.pdf} -\caption{Varying leaves and estimators: effect on accuracy} -\label{fig:p3_colormap} -\end{center} -\end{figure} - # Comparison of methods and conclusions Overall we observe slightly higher accuracy when using K-means codebooks compared to RF codebook at the expense of a higher execution time for training and testing. @@ -174,6 +159,16 @@ is the one that gets misclassified the most, both in k-means and RF-codebook (re from this class do not guarantee very discriminative splits, hence the first splits in the trees will prioritize features taken from other classes. +# Appendix + +\begin{figure}[H] +\begin{center} +\includegraphics[width=14em]{fig/p3_colormap.pdf} +\caption{Varying leaves and estimators: effect on accuracy} +\label{fig:p3_colormap} +\end{center} +\end{figure} + # References |