# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 5 Conclusions  
This paper proposes a family of novel saliency-informed local feature descriptor extraction approaches for egocentric action recognition. The performance of the proposed family of feature descriptors was evaluated using the UNN-GazeEAR dataset. The experimental results demonstrate the effectiveness of the proposed method in improving the performance of the original LFD approaches. This indicates that the saliency map can be a useful ingredient for extracting local spatial and temporal features in the recognition of first-person video actions. Possible future work includes more large-scale applications, which are required to further evaluate the proposed approach. It would also be interesting to investigate how the saliency map can support global feature descriptors. Another interesting potential investigation would be broadening the applicability of the proposed feature descriptors for object detection using an electromagnetic source [(43; 44; 45)] (e.g., signals, pictures of vehicles, antennas, etc.).  
Conceptualization, L.Y. and Z.Z.; methodology, Z.Z., F.C. and Y.Q.; software, Z.Z.; validation, L.Y., B.W. and Y.P.; writing-original draft preparation, Z.Z.; writing-review and editing, B.W., F.C., Y.Q., Y.P., and L.Y.; supervision, L.Y.  
This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{3}{*}{**FeatDesc**} & \multirow{3}{*}{**FeatType**} & \multicolumn{6}{c}{**UNN-GazeEAR @1920 \(\times\) 1080**} \\ \cline{3-10}  & & \multicolumn{2}{c}{[16 \(\times\) 16 \(\times\) 6]} & \multicolumn{2}{c}{[32 \(\times\) 32 \(\times\) 6]} & \multicolumn{2}{c}{[64 \(\times\) 64 \(\times\) 6]} \\ \cline{3-10}  & & \(T^{E}\) & _VLAD_ & _FV_ & \(T^{E}\) & _VLAD_ & _FV_ & \(T^{E}\) & _VLAD_ & _FV_ \\ \hline GROI-HOG [(5)] & \(\mathcal{S}\) & 865 & **95.60** & **97.28** & 817 & **94.40** & **95.18** & 802 & **93.94** & **93.74** \\ SM-HOG & \(\mathcal{D}\) & 3611 & 87.24 & 82.92 & 3724 & 88.84 & 84.46 & 3716 & 87.32 & 84.78 \\ \hline GROI-HMG [(5)] & \(\mathcal{S}\) & 859 & **95.94** & **95.82** & 815 & **94.68** & **92.52** & 802 & **93.64** & **93.62** \\ SM-HMG & \(\mathcal{D}\) & 3541 & 88.84 & 84.22 & 3537 & 87.52 & 84.28 & 3542 & 87.54 & 85.64 \\ \hline GROI-HOF [(5)] & \(\mathcal{S}\) & 1077 & **94.58** & **95.30** & 1021 & **96.00** & **94.28** & 1011 & **95.02** & **93.64** \\ SM-HOF & \(\mathcal{D}\) & 3878 & 88.06 & 86.68 & 3744 & 87.10 & 83.70 & 3675 & 86.74 & 85.60 \\ \hline GROI-MBHx [(5)] & \(\mathcal{S}\) & 1767 & **92.78** & **95.82** & 1579 & **93.20** & **95.28** & 1559 & **94.50** & **95.08** \\ SM-MBHx & \(\mathcal{D}\) & 6688 & 83.68 & 83.00 & 6647 & 85.40 & 82.94 & 6643 & 85.60 & 86.16 \\ \hline GROI-MBHy [(5)] & \(\mathcal{S}\) & 1764 & **97.44** & **94.72** & 1575 & **94.92** & **93.28** & 1558 & **94.10** & **93.98** \\ SM-MBHy & \(\mathcal{D}\) & 6678 & 83.26 & 84.20 & 6647 & 85.64 & 83.20 & 6640 & 85.92 & 84.44 \\ \hline \hline \end{tabular} \(\mathcal{D}\): Dense features; \(\mathcal{S}\): Sparse features \(T^{E}\): time required for feature extraction (measured in seconds).  
\end{table}
Table 6: Comparison with other feature descriptors.