From ed2219fbb0a66c5e6d6eccad58c131e2d1ff299c Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Mon, 11 Feb 2019 21:59:01 +0000
Subject: Fix position of figures

---
 report/paper.md | 188 ++++++++++++++++++++++++--------------------------------
 1 file changed, 79 insertions(+), 109 deletions(-)

(limited to 'report')

diff --git a/report/paper.md b/report/paper.md
index e673adf..7453289 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -13,193 +13,163 @@ Caltech dataset.
 
 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
-
-# RF classifier 
-
-Train and test Random Forest using the training and testing data set in the form of bag-of-words
-obtained in Q1. Change the RF parameters (including the number of trees, the depth of trees, the
-degree of randomness parameter, the type of weak-learners: e.g. axis-aligned or two-pixel test),
-and show and discuss the results:
-
-## recognition accuracy, confusion matrix,
-
-## example success/failures,
-
-## time-efficiency of training/testing, 
-
-## impact of the vocabulary size on classification accuracy. 
-
-# RF codebook
-
-In Q1, replace the K-means with the random forest codebook, i.e. applying RF to 128 dimensional
-descriptor vectors with their image category labels, and using the RF leaves as the visual
-vocabulary. With the bag-of-words representations of images obtained by the RF codebook, train
-and test Random Forest classifier similar to Q2. Try different parameters of the RF codebook and
-RF classifier, and show/discuss the results in comparison with the results of Q2, including the
-vector quantisation complexity. 
-
-# Pictures
+Looking at picture \ref{fig:histo_te}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/256t1_e200D5_cm.pdf}
-\caption{Part 3 confusion matrix e100k256d5cm}
-\label{fig:rerank}
+\includegraphics[height=4em]{fig/hist_test.jpg}
+\includegraphics[width=20em]{fig/km-histogram.pdf}
+\caption{Bag-of-words Training histogram}
+\label{fig:histo_tr}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/Depth_Trees_P2.pdf}
-\caption{DepthTreesP2}
-\label{fig:rerank}
+\includegraphics[height=4em]{fig/hist_train.jpg}
+\includegraphics[width=20em]{fig/km-histtest.pdf}
+\caption{Bag-of-words Testing histogram}
+\label{fig:histo_te}
 \end{center}
 \end{figure}
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=18em]{fig/Depth_Trees_P3.pdf}
-\caption{DepthTreesP3}
-\label{fig:rerank}
-\end{center}
-\end{figure}
+## Vector quantisation process
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=18em]{fig/Depth_Trees_P3_fixedestimators.pdf}
-\caption{DepthTreesP3fixedestimators}
-\label{fig:rerank}
-\end{center}
-\end{figure}
+# RF classifier 
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=18em]{fig/e100k256d5_cm.pdf}
-\caption{e100k256d5cm Kmean Confusion Matrix}
-\label{fig:rerank}
-\end{center}
-\end{figure}
+## Hyperparameters tuning
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=18em]{fig/error_depth_kmean100.pdf}
-\caption{errordepthkmean100}
-\label{fig:rerank}
-\end{center}
-\end{figure}
+Figure \ref{fig:km-tree-param} shows the effect of tree depth and number of trees
+for kmean 100 cluster centers.
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/error_depth_p3.pdf}
-\caption{errordepthp3}
-\label{fig:rerank}
+\includegraphics[width=12em]{fig/error_depth_kmean100.pdf}
+\includegraphics[width=12em]{fig/trees_kmean.pdf}
+\caption{Classification error varying trees depth(left) and numbers of trees(right)}
+\label{fig:km-tree-param}
 \end{center}
 \end{figure}
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=18em]{fig/kmean_rand.pdf}
-\caption{kmeanrand}
-\label{fig:rerank}
-\end{center}
-\end{figure}
+Figure \ref{fig:kmeanrandom} shows randomness parameter for kmean 100.
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/kmeans_vocsize.pdf}
-\caption{kmeansvocsize}
-\label{fig:rerank}
+\includegraphics[width=18em]{fig/new_kmean_random.pdf}
+\caption{newkmeanrandom}
+\label{fig:kmeanrandom}
 \end{center}
 \end{figure}
 
+## Weak Learners comparison
+
+In figure \ref{fig:2pt} it is possible to notice an improvement in recognition accuracy by 1%,
+with the two pixels test, achieving better results than the axis-aligned counterpart. The two-pixels
+test however brings a slight deacrease in time performance which has been measured to be on **average 3 seconds**
+more. This is due to the complexity added by the two-pixels test, since it adds one dimension to the computation.
+
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/new_kmean_random.pdf}
-\caption{newkmeanrandom}
-\label{fig:rerank}
+\includegraphics[width=18em]{fig/2pixels_kmean.pdf}
+\caption{Kmean classification accuracy changing the type of weak learners}
+\label{fig:2pt}
 \end{center}
 \end{figure}
 
+## Impact of the vocabulary size on classification accuracy. 
+
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/p3_colormap.pdf}
-\caption{p3colormap}
-\label{fig:rerank}
+\includegraphics[width=12em]{fig/kmeans_vocsize.pdf}
+\includegraphics[width=12em]{fig/time_kmeans.pdf}
+\caption{Effect of vocabulary size; classification error left, time right}
+\label{fig:km_vocsize}
 \end{center}
 \end{figure}
 
+## Confusion matrix for case XXX, with examples of failure and success 
+
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/p3_rand.pdf}
-\caption{p3rand}
-\label{fig:rerank}
+\includegraphics[width=18em]{fig/e100k256d5_cm.pdf}
+\caption{e100k256d5cm Kmean Confusion Matrix}
+\label{fig:km_cm}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/p3_time.pdf}
-\caption{p3time}
-\label{fig:rerank}
+\includegraphics[width=10em]{fig/success_km.pdf}
+\includegraphics[width=10em]{fig/fail_km.pdf}
+\caption{Kmean: Success on the left; Failure on the right}
+\label{fig:km_succ}
 \end{center}
 \end{figure}
 
+# RF codebook
+
+In Q1, replace the K-means with the random forest codebook, i.e. applying RF to 128 dimensional
+descriptor vectors with their image category labels, and using the RF leaves as the visual
+vocabulary. With the bag-of-words representations of images obtained by the RF codebook, train
+and test Random Forest classifier similar to Q2. Try different parameters of the RF codebook and
+RF classifier, and show/discuss the results in comparison with the results of Q2, including the
+vector quantisation complexity. 
+
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/p3_vocsize.pdf}
-\caption{p3vocsize}
-\label{fig:rerank}
+\includegraphics[width=18em]{fig/256t1_e200D5_cm.pdf}
+\caption{Part 3 confusion matrix e100k256d5cm}
+\label{fig:p3_cm}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/time_kmeans.pdf}
-\caption{timekmeans}
-\label{fig:rerank}
+\includegraphics[width=10em]{fig/success_3.pdf}
+\includegraphics[width=10em]{fig/fail_3.pdf}
+\caption{Part3: Success on the left; Failure on the right}
+\label{fig:p3_succ}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/trees_kmean.pdf}
-\caption{treeskmean}
-\label{fig:rerank}
+\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)}
+\label{fig:p3_trees}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=18em]{fig/trees_p3.pdf}
-\caption{treesp3}
-\label{fig:rerank}
+\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=10em]{fig/success_km.pdf}
-\includegraphics[width=10em]{fig/fail_km.pdf}
-\caption{Kmean: Success on the left; Failure on the right}
-\label{fig:rerank}
+\includegraphics[width=12em]{fig/p3_vocsize.pdf}
+\includegraphics[width=12em]{fig/p3_time.pdf}
+\caption{Effect of vocabulary size; classification error left, time right}
+\label{fig:p3_voc}
 \end{center}
 \end{figure}
 
 \begin{figure}[H]
 \begin{center}
-\includegraphics[width=10em]{fig/success_3.pdf}
-\includegraphics[width=10em]{fig/fail_3.pdf}
-\caption{Part3: Success on the left; Failure on the right}
-\label{fig:rerank}
+\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
+
 # References
 
 
-- 
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