# Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders
## 6 Experimental Results
### Dance Summarization Experiments
The H-SMRS scheme also provides much better performance for the Syrtos(3b) dance, which seems to be more complicated than the other two dances, resulting in higher time deviations for the rest of the samplers. It is also worth mentinign the complex effect of coupling different features and samplers. For example, Syrtos(2b) input type does not affect significantly the performance for all four samplers. Table 3 shows the average time deviation of key frames extracted by the four summarization algorithms and the ground truth data, that is, the value \(\mu\), measured, however, in seconds and not in frame index differences just for clarity. As is observed, the best performance is given for the the H-SMRS algorithm when the SAE scheme is used. In particular, the highest deviation of the H-SMRS \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dance Sequence** & \begin{tabular}{c} **Max Correlation Without** \\ **Corrupted Frames** \\ \end{tabular} & \
\begin{tabular}{c} **Max Correlation with** \\ **Corrupted Frames** \\ \end{tabular} &
\begin{tabular}{c} **Sampling Summarization** \\ **Algorithm** \\ \end{tabular} \\ \hline \multirow{4}{*}{Makedonikos} & 0.64 & 0.52 & KenStone \\  & 0.61 & 0.47 & K-OPTICS \\  & 0.65 & 0.53 & SMRS \\  & 0.65 & 0.53 & H-SMRS \\ \hline \multirow{4}{*}{Syrtos (2-beats)} & 0.30 & 0.29 & KenStone \\  & 0.64 & 0.51 & K-OPTICS \\  & 0.57 & 0.43 & SMRS \\  & 0.57 & 0.43 & H-SMRS \\ \hline \multirow{4}{*}{Syrtos (3-beats)} & 0.63 & 0.50 & KenStone \\  & 0.60 & 0.48 & K-OPTICS \\ \cline{1-1}  & 0.57 & 0.43 & SMRS \\ \cline{1-1}  & 0.57 & 0.43 & H-SMRS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum correlation coefficient scores (\(\rho_{i}^{max}\)) for different sampling algorithms and dance sequences. Figure 10: Data input type summarization impact when two dancers performed simultaneously for all the examined dance sequences. is achieved for the Syrtos (3b) equal to 0.26 s deviation on average which is in fact a very small deviation value. Similar performances of 0.23 and 0.24 sec deviations is also noticed for the other two dances. In the same table, we also present the standard deviation of the time shift to the ground truth data to show how these values vary. Again, H-SMRS yields the smallest standard deviation values which is about 0.18 s using the SAE, revealing its robustness against the other compared summarization algorithms. In the same table, we illustrate the results without using the SAE scheme. All summarization approaches, except KenStone algorithm, provide better results when the SAE-based compression framework is adopted. We get better scores in both average time shift and standard deviation, compared to the expert's annotated frames. For the Kenstone algorithm and only for two out of three dances, the performance remains, approximately the same, regardless of using or not the SAE. Table 4 shows how much the average time shift of the four examined summarization algorithms and the ground truth data is improved when the SAE-based compressed scheme is applied on the raw 3D data in case of Syrtos (3b) dance sequence. The results have been depicted for two different executions of the dance, one with a single dancer and one with two dancers. It is observed that in case of a two dancers' performance the improvement ratio is much greater than the single dancer performance execution. Moreover, the adoption of the H-SMRS combined with SAE schema exhibits great improvement which reaches 81.80%.