# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 4 Experiments and Results
### Experiment Using the Memory Aid Dataset  
In this experiment, the trained models (with which the SMLFD features were extracted) were applied to an untrimmed video stream that was published in [5]. Table 5 shows that the SMLFD-trained  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FeatEnc** & **FeatDesc** & **TopAcc** & **BlockSize** & **FeatDim** & **FeatDesc** & **TopAcc** & **BlockSize** & **FeatDim** \\ \hline \multirow{5}{*}{VLAD} & SM-HOG & 99.16 & [4 \(\times\) 4 \(\times\) 6] & 48 & GROI-HOG [5] & 96.50 & [16 \(\times\) 16 \(\times\) 6] & 18 \\  & SM-HMG & 99.02 & [4 \(\times\) 4 \(\times\) 6] & 60 & GROI-HMG [5] & 97.32 & [8 \(\times\) 8 \(\times\) 6] & 6 \\  & SM-HOF & 99.36 & [4 \(\times\) 4 \(\times\) 6] & 24 & GROI-HOF [5] & 95.36 & [8 \(\times\) 8 \(\times\) 6] & 18 \\  & SM-MBHx & 97.16 & [8 \(\times\) 8 \(\times\) 6] & 18 & GROI-MBHx [5] & 96.46 & [16 \(\times\) 16 \(\times\) 6] & 6 \\  & SM-MBHy & 98.24 & [8 \(\times\) 8 \(\times\) 6] & 36 & GROI-MBHy [5] & 96.54 & [16 \(\times\) 16 \(\times\) 6] & 24 \\ \hline \multirow{5}{*}{FV} & SM-HOG & 99.34 & [4 \(\times\) 4 \(\times\) 6] & 24 & GROI-HOG [5] & 95.04 & [16 \(\times\) 16 \(\times\) 6] & 60 \\  & SM-HMG & 98.76 & [4 \(\times\) 4 \(\times\) 6] & 9 & GROI-HMG [5] & 95.24 & [8 \(\times\) 8 \(\times\) 6] & 36 \\  & SM-HOF & 98.58 & [4 \(\times\) 4 \(\times\) 6] & 72 & GROI-HOF [5] & 96.68 & [8 \(\times\) 8 \(\times\) 6] & 60 \\  & SM-MBHx & 97.04 & [4 \(\times\) 4 \(\times\) 6] & 36 & GROI-MBHx [5] & 95.06 & [16 \(\times\) 16 \(\times\) 6] & 9 \\  & SM-MBHy & 97.02 & [16 \(\times\) 16 \(\times\) 6] & 60 & GROI-MBHy [5] & 95.16 & [8 \(\times\) 8 \(\times\) 6] & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the top performing feature descriptor with that of the other feature descriptors for different feature encoding schemes on the UNN-GazeEAR dataset. The global best accuracies in both (SMFLD and GROILFD) are marked with gray color.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BlockSize**} & \multirow{2}{*}{**FeatEnc**} & \multicolumn{8}{c}{**UNN-GazeEAR with SMLFD @320 \(\times\) 180**} \\ \cline{3-10}  & & **3** & **6** & **9** & **18** & **24** & **36** & **48** & **60** & **72** \\ \hline \multirow{2}{*}{\([4\times 4\times 6]\)} & VLAD & 93.82 & 95.96 & 97.76 & 97.40 & 99.36 & 98.72 & 99.16 & 99.02 & 98.86 \\  & FV & 93.98 & 96.10 & 98.76 & 98.40 & 99.34 & 98.84 & 99.00 & 99.06 & 99.10 \\ \hline \multirow{2}{*}{\([8\times 8\times 6]\)} & VLAD & 95.78 & 97.42 & 95.46 & 97.20 & 97.30 & 98.24 & 98.10 & 97.74 & 97.34 \\  & FV & 95.72 & 96.08 & 98.22 & 97.28 & 97.46 & 96.94 & 96.78 & 97.06 & 96.80 \\ \hline \multirow{2}{*}{\([16\times 16\times 6]\)} & VLAD & 95.88 & 95.68 & 96.24 & 96.50 & 96.68 & 95.92 & 96.04 & 96.08 & 96.52 \\  & FV & 94.52 & 96.40 & 96.22 & 96.06 & 97.08 & 96.90 & 96.92 & 97.02 & 96.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison of saliency map-enhanced local feature descriptors (SMLFDs) using different feature dimensions with various block sizes (the adopted color schemes are consist with Figure 2).  
Figure 3: Statistics of the best performance obtained for each employed block size. (\(\cdot\)) denotes the number of best performances that were equivalently achieved by other feature descriptors in the SMLFD family. (**a**) [\(4\times 4\times 6\)]; (**b**) [\(8\times 8\times 6\)]; (**c**) [\(16\times 16\times 6\)].  
models possessed better performance compared with that trained using GROILFD features. In general, SMLFD performed as well as GROILFD. Each of them had three superior performances and four equivalent performances. Similarly, both SM-HOG and GROI-HOG achieved 100% accuracy using FV feature encoding. The three higher results yielded by GROILFD were produced by GROI-HOG, GROI-MBHx, and GROI-MBHy, all under VLAD feature encoding.