# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RF** & **Fencing** & **Walk** & **Punch** & **Smoke** & **Cartwheel** \\ \hline Brush\_hair & HOF: 0.7576 & HOF: **0.8607** & HOF: **0.8680** & HOF: 0.6894 & HOF: 0.9535 \\ \cline{2-6}  & Variance: & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.8413 & q = 3: 0.7540 & q = 3: 0.8095 & q = 3: 0.7143 & q = 3: 0.9286 \\  & q = 5: 0.8730 & q = 5: 0.6984 & q = 5: 0.8254 & q = 5: 0.6587 & q = 5: 0.9445 \\  & q = 10: 0.8651 & q = 10: 0.7698 & q = 10: 0.8254 & q = 10: 0.7302 & q = 10: 0.9445 \\ \cline{2-6}  & More info: & More info: & More info: & More info: \\  & q = 3: **0.9683** & q = 3: 0.7460 & q = 3: 0.8492 & q = 3: **0.8016** & q = 3: **0.9841** \\  & q = 5: 0.9603 & q = 5: 0.7222 & q = 5: 0.8016 & q = 5: 0.7619 & q = 5: 0.9762 \\  & q = 10: 0.9445 & q = 10: 0.8174 & q = 10: 0.8254 & q = 10: 0.7857 & q = 10: 0.9762 \\ \hline  & & HOF: 0.7553 & HOF: 0.8889 & HOF: 0.8106 & HOF: 0.9380 \\ \cline{2-6}  & & Variance: & Variance: & Variance: & Variance: \\  & q = 3: 0.7391 & q = 3: 0.7681 & q = 3: 0.7576 & q = 3: 0.8571 \\ Fencing & & q = 5: 0.7391 & q = 5: 0.7464 & q = 5: 0.7348 & q = 5: 0.8016 \\  & & q = 10: 0.7391 & q = 10: 0.7391 & q = 10: 0.8030 & q = 10: 0.8889 \\ \cline{2-6}  & & More info: & More info: & More info: & More info: \\  & q = 3: 0.9058 & q = 3: 0.8406 & q = 3: 0.8561 & q = 3: **0.9445** \\  & q = 5: 0.8913 & q = 5: 0.8696 & q = 5: 0.8561 & q = 5: 0.8889 \\  & q = 10: **0.9130** & q = 10: **0.9058** & q = 10: **0.8712** & q = 10: 0.8412 \\ \hline  & & HOF: **0.7912** & HOF: **0.7384** & HOF: 0.9103 \\ \cline{2-6}  & & Variance: & Variance: & Variance: \\  & q = 3: 0.6800 & q = 3: 0.4924 & q = 3: 0.8413 \\  & q = 5: 0.6667 & q = 5: 0.6288 & q = 5: 0.8730 \\ Walk & & q = 10: 0.6000 & q = 10: 0.5909 & q = 10: 0.7937 \\ \cline{2-6}  & & More info: & More info: & More info: \\  & q = 3: 0.6467 & q = 3: 0.6212 & q = 3: **0.9841** \\  & q = 5: 0.6733 & q = 5: 0.6439 & q = 5: 0.9683 \\  & q = 10: 0.6667 & q = 10: 0.5758 & q = 10: 0.9445 \\ \hline  & & HOF: **0.8403** & HOF: 0.9504 \\ \cline{2-6}  & & Variance: & Variance: \\  & q = 3: 0.6742 & q = 3: 0.8651 \\ Punch & & q = 5: 0.7500 & q = 5: 0.8968 \\  & & q = 10: 0.7348 & q = 10: 0.8492 \\ \cline{2-6}  & & More info: & More info: \\  & q = 3: 0.6894 & q = 3: **0.9841** \\  & q = 5: 0.7500 & q = 5: 0.9762 \\ q = 10: 0.7424 & q = 10: **0.9841** \\ \hline  & & HOF: 0.8992 \\ \cline{2-6}  & & Variance: & Variance: \\  & q = 3: 0.8095 \\  & q = 5: 0.9048 \\  & q = 10: 0.8730 \\ \hline  & & More info: & \\  & q = 3: 0.9445 \\  & q = 5: **0.9762** \\  & q = 10: **0.9762** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RF classifier results. Analyzing the results, it can be seen that the KNN classifier obtains the worst results. The other two classifiers, LDA and RF, achieve more similar results, although results with RF classifier are, in general, better. That is, for most pairs of classes, the KNN classifier gets the lowest accuracy values. The best accuracy for each pair is sometimes achieved by LDA and other times by RF, RF being the one which gets the best outcomes most of the times. Regarding the feature extraction techniques, there is not a clear winner. The best results are highlighted in the mentioned tables. As it can be seen, depending on the targets and the classifiers, one technique or the other is preferred. There is not much difference between the use of the different features. As the obtained results are not clear enough to determine which features are better to use, a statistical test is performed to compare them. Before performing the comparison, another aspect of the classification must be mentioned, the target classes. Depending on the classes that are being classified, the results may vary, because some pairs can be more distinguishable than others. For instance, the pair _brush hair_ and _cartwheel_ classes get high accuracy values for every technique and algorithm, with a mean of 0.95.