Merge branch 'master' of github.com:Dekker1/ResearchMethods

mini_proj/report/waldo.tex

@@ -38,10 +38,10 @@
 		understand. In this report we compare the well known machine learning
 		methods Naive Bayes, Support Vector Machines, $k$-Nearest Neighbors, and
 		Random Forest against the Neural Network Architectures LeNet, Fully
-		Convolutional Neural Networks, and Fully Convolutional Neural Networks.
+		Convolutional Neural Networks, and Fully Convolutional Neural Networks. Our
-		\todo{I don't like this big summation but I think it is the important
+		comparison shows that, although the different neural networks architectures
-		information}
+		have the highest accuracy, some other methods come close with only a
-		Our comparison shows that \todo{...}
+		fraction of the training time.
 
 		\end{abstract}
 
@@ -289,7 +289,10 @@
 		perform poorly in either precision or recall.
 
 		\section{Results} \label{sec:results}
-		\tab The time taken to train each of the neural networks and traditional approaches was measured and recorded alongside their accuracy (evaluated using a separate test dataset) in Table \ref{tab:results}.
+
+		The time taken to train each of the neural networks and traditional
+		approaches was measured and recorded alongside their accuracy (evaluated
+		using a separate test dataset) in Table \ref{tab:results}.
 
 		% Annealing image and caption
 		\begin{table}[H]
@@ -301,7 +304,7 @@
 				\hline
 				LeNet & 87.86\% & 65.67\\
 				\hline
-				CNN & 95.63\% & 119.31\\
+				CNN & \textbf{95.63\%} & 119.31\\
 				\hline
 				FCN & 94.66\% & 113.94\\
 				\hline
@@ -309,18 +312,35 @@
 				\hline
 				K Nearest Neighbours & 67.96\% & 0.22\\
 				\hline
-				Gaussian Naive Bayes & 85.44\% & 0.15\\
+				Gaussian Naive Bayes & 85.44\% & \textbf{0.15}\\
 				\hline
 				Random Forest & 92.23\% & 0.92\\
 				\hline
 			\end{tabular}
 			\captionsetup{width=0.70\textwidth}
-			\caption{Comparison of the accuracy and training time of each neural network and traditional machine learning technique}
+			\caption{Comparison of the accuracy and training time of each neural
+				network and traditional machine learning technique}
 			\label{tab:results}
 		\end{table}
 
 		\section{Conclusion} \label{sec:conclusion}
 
+		Image from the ``Where's Waldo?'' puzzle books are ideal images to test
+		image classification techniques. Their tendency for hidden objects and ``red
+		herrings'' make them challenging to classify, but because they are drawings
+		they remain tangible for the human eye.
+
+		In our experiments we show that, given unspecialized methods, Neural
+		Networks perform best on this kind of image classification task. No matter
+		which architecture their accuracy is very high. One has to note though that
+		random forest performed surprisingly well, coming close to the performance
+		of the better Neural Networks. Especially when training time is taking into
+		account it is the clear winner.
+
+		It would be interesting to investigate various of these methods further.
+		There might be quite a lot of ground that could be gained by using
+		specialized variants of these clustering algorithms.
+
 		\clearpage          % Ensures that the references are on a seperate page
 		\pagebreak
 		\bibliographystyle{alpha}