# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 4 Results
In Figure 12, the performance for other methods in the literature, in particular, the original SIFT [7] with non-maxima suppression (i.e., one feature on the template (left side), which can have multiple matches in the query (right side)) and original SURF [36], is shown. As can be seen, by comparing only feature points or blobs, low performance is achieved. In both cases, several mismatches or multiple matches for the same feature are generated. This demonstrates the low performance that point information can generate. On the other hand, our approach in Figures 10 and 11, which is based on shape and gradient, involves higher robustness for the matching process. This is shown in Table 1, in which average accuracy values by considering multiple test for several templates are presented. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Accuracy** \\ \hline SIFT [7] & 43\% \\ SURF [36] & 69\% \\ FAST [37] & 60\% \\ ORB [9] & 67\% \\ This work & 81\% \\ \hline \end{tabular}
\end{table} 
Table 1: Mean accuracy for the proposed algorithm compared with previous approaches in the literature. Figure 11: Object detection by the proposed method under complex visual variations/rotations (**a**) and complex texture objects (**b**). In both cases, high accuracy is reached. Mismatches are marked as red circles. Figure 10: Object detection by the proposed method. Different rotations between template and query (**a**,**b**,**d**) and scale variations (**c**) were tested. In all cases, accurate detection can be reached. Mismatches are marked as red circles. In Figures 13 and 14, results for scene recognition are shown. Again, when compared to our approach (Figure 13) with the original SIFT (Figure 14), our approach clearly outperforms previous work. In our work, most important mismatches are generated for some cloud patterns in the template which match the tree patterns in the query. Again, this is as expected, since matched patterns have the same shape and gradient. On the other hand, SIFT generated several mismatches, and in this case, it could be difficult to determine if the scene was recognized or not. Finally, in Tables 2 and 3, processing speed comparisons are presented. Our approach, which consists of complex features (compared with points which are the features used by previous approaches), requires more processing time. However, even with this complexity, our algorithms outperform the original ORB. Considering that algorithms like ORB have been successfully used in real-time applications such as SLAM [38], robotics [39], and so on, we consider our approach can be successfully used. Note that our algorithm was implemented in MATLAB, without hardware acceleration or advanced programming patterns, while the other algorithms have been highly improved in terms of implementation in order to reach optimal processing speed. We believe our approach can be improved in terms of implementation, and this should increase the processing speed. This will be addressed in future work. Figure 12: Object detection by other algoritmms in the literature. (**a**) original SIFT [7], (**b**) original SURF. [36]. Figure 13: Results for scene recognition: (**a**) Indoor scenario: only two mismatches are generated. In both cases, features that were matched present similarities in terms of shape and gradient. (**b**) Outdoor scenario: the most important mismatches are generated for some cloud patterns in the template, which match the tree patterns in the query. Again, this is as expected, since matched patterns have the same shape and gradient. **Limitations:** Since the direction in the proposed Directional Pixel Voting Descriptor is defined in the 2D image pixel coordinate, rotations which deforms or occlude primitive shapes in template are not supported, as shown in Figure 15a. However, even classical methods such as SIFT or SURF suffer from this in the case of textureless objects Figure 15b. Asa final discussion, experimental results demonstrate the effectiveness and robustness of our proposed approach for object and scene recognition tasks.