# A Distributed Automatic Video Annotation Platform
## 5 Evaluation and Analysis
### Experimental Analysis  
Figure 11 shows the scalability of the proposed distributed video annotation APIs. We have developed and implemented distributed feature extraction APIs. For instance, local binary pattern (LBP) [40], local directional ternary pattern (LDTP) [41], local ternary pattern (LTP) [42], color local binary pattern (CLBP), and color local directional ternary pattern (CLDTP) were developed for spatial feature extraction, and volume local binary pattern (VLBP) [44], local binary pattern-three \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Sub-Category** & **Dynamic Information** & **Spatial Appearances** \\ \hline \multirow{6}{*}{Human Action} & BaseballPitch & Person, Ball, Field, Grass/Green\_field, Sky \\ \cline{2-4}  & \multirow{2}{*}{Biking} & Person, Cycle, Tree, Car, Road, Fence, Sky, \\ \cline{3-4}  & & & Grass/Green\_field \\ \cline{2-4}  & \multirow{2}{*}{Horse Riding} & Person, Horse, Tree, Bush, Fence, Sky, \\ \cline{3-4}  & & & Grass/Green\_field \\ \cline{3-4}  & & & Skate Boarding \\ \cline{3-4}  & & & \\ \cline{3-4}  & & Swing & Person, Tree, Bush, Grass/Green\_field \\ \cline{2-4}  & \multirow{2}{*}{Crowd Movement} & Band Marching & Person, Road, Tree, Sky, Building \\ \cline{3-4}  & & & Run \\ \hline \multirow{2}{*}{Emergency} & \multirow{2}{*}{Explosions} & Person, Fire, Building, Sky, Hill, Fire, Volcano \\ \cline{3-4}  & & & Tornado \\ \cline{3-4}  & & & Granado, Grass/Green\_field, Stamp \\ \hline \multirow{2}{*}{Traffic} & \multirow{2}{*}{Car} & Car & Car, Road, Sky, Tree, Bush, Building, \\ \cline{3-4}  & & & Grass/Green\_field \\ \hline \multirow{2}{*}{Nature} & \multirow{2}{*}{Birds Fly} & Birds, Sky, Person, Building, Sea, \\ \cline{3-4}  & & & Grass/Green\_field \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of proposed STAD video dataset. Figure 10: Sample frames of the proposed STAD video dataset. orthogonal planes (LBP-TOP) [44], motion binary pattern (MBP) [45], and volume local directional ternary pattern-three orthogonal planes (VLDTP-TOP) were developed for dynamic feature extraction respectively. Furthermore, we have used the distributed deep learning for Java (DL4J) [49] for developing the CNN APIs. We also implemented a distributed similarity measure nearest neighbor algorithm for both the spatial and temporal features. Scalability has been tested on the proposed STAD dataset. Figure 11 shows that for one node it takes more time than employing three nodes. With the increase of nodes, the processing time decreases, which proves the scalability of the platform. From Figure 11a we can observe that, for the temporal feature extraction, MBP takes less time than the other descriptors in all three cases. MBP takes 213 s in one node and 64 s in three nodes. The proposed VLDTP-TOP takes more time than the existing descriptors due to the computation complexity. VLDTP-TOP takes 778 s in one node and its performance is increased when three nodes are employed. However, VLDTP-TOP outperforms in terms of average precision compared the existing descriptors. The LBP-based texture feature descriptor takes less time than other descriptors due to the simplicity of the algorithm. LBP took 97 s in one node while its performance was improved when three nodes were utilized and it took only 30 s. CLDTP with the original mask took 251 s with one node and 84 s for three nodes. However, the proposed CLDTP with a modified Frei-Chen mask took 183 s in one node and performance increased when using three nodes. Figure 11c shows that the performance of the nearest neighbor algorithm increased with the number of nodes. For one node the the nearest neighbor algorithm took 243 s (temporal) and 66.8 s (spatial), whereas for three nodes it took 95 s and 25 s for the temporal and spatial annotation respectively. Figure 11: Scalability of the low-level APIs for distributed video annotation on the STAD dataset. (**a**) Time (in seconds) required for the dynamic feature extraction and (**b**) time (in seconds) required for the spatial feature extraction.