From 81054d171b6f68551a564054fd6edbd380b8d69d Mon Sep 17 00:00:00 2001 From: Silver-T Date: Fri, 25 May 2018 19:32:21 +1000 Subject: [PATCH] Report edit #n --- mini_proj/report/waldo.tex | 35 +++++++++++++++-------------------- 1 file changed, 15 insertions(+), 20 deletions(-) diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex index 90d7c75..84b2494 100644 --- a/mini_proj/report/waldo.tex +++ b/mini_proj/report/waldo.tex @@ -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}