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Silver-T 2018-05-25 19:32:21 +10:00
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mini_proj/report/waldo.tex
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@ -299,7 +299,7 @@
\hline
Gaussian Naive Bayes & 85.44\% & \textbf{0.15}\\
\hline
Random Forest & 92.23\% & 0.92\\
Random Forest & 95.14\% & 0.27\\
\hline
\end{tabular}
\captionsetup{width=0.80\textwidth}
@ -324,23 +324,18 @@
\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 separate page
\pagebreak
\bibliographystyle{alpha}
\bibliography{references}
\tab 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, and the density of detail they contain makes them interesting to approach with machine learning.
\\
\par
In our experiments we show a comparison of machine learning methods, including deep learning, for the task of classifying an image as containing Waldo or not.
The convolutional neural network architecture performed best at this task with an accuracy of 95.63\% followed closely by the random forest approach with an accuracy of 95.14\%. The random forest however, had a much lower training time of 0.27. Considering the training time, the random forest approach would appear to be most suited to the task.
\\
\par
It would be interesting to investigate various of these methods further, including further varying the hyperparameter in the neural networks.
However, there may also be much more insight to be gained by exploring the classical algorithms.
\clearpage % Ensures that the references are on a separate page
\pagebreak
\bibliographystyle{alpha}
\bibliography{references}
\end{document}