# Heterogeneous Image Matching via a Novel Feature Describing Model
## 4 Experiments
Figure 7.0 Results analysis of the scaled and rotated images matching test: (**a**) correct matching number; (**b**) cost time per match; (**c**) precision of the matches; (**d**) recall of the matches. Figure 7.1 Matching results of heterogeneous images: (**a**) [PERSON] results; (**b**) SIFT results; (**c**) PCA-SIFT results; (**d**) our results. Based on the four experiments above, we use three performance levels (Low, Moderate, High) to comprehensively evaluate the methods as shown in Figure 7.2 It can be found the proposed method is advantageous in mixed changed image matching and heterogeneous image matching. For more comprehensive results from the proposed method, we carry out these methods on the Oxford database in the last experiment. **Matching test on Oxford database**. Eight image pairs are selected from the well-known feature matching test database Oxford provided by [PERSON] et al. [11]. All the images are scaled to one-third of the original size, and the right one of each pair is transformed into a heterogeneous version as shown in Figure 7.3 After applying the proposed method and the compared methods on each image pair, the matching results can be obtained as shown in Figure 7.4 Harris finds few matches and the proposed method finds the most matches in each pair. SIFT and PCA-SIFT can find moderate matches. Our method can find more matches than the other three methods in each pair as shown in Figure 15a and Figure 7.5 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Changes** & **Noise** & **Rotation + Noise** & **Rotation + Scale** & **Heterogeneous** \\ \hline Harris & Low & Low & Low & Low \\ SIFT & High & Moderate & Moderate & Moderate \\ PCA-SIFT & Moderate & High & Moderate & Moderate \\ Proposed & High & High & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance levels of correct number. Figure 13: Results analysis of the heterogeneous and noised images matching test: (**a**) correct matching number; (**b**) cost time per match; (**c**) precision of the matches; (**d**) recall of the matches. As shown in Figure 15b, the proposed method achieves better performance on cost time than Harris and SIFT but worse than PCA-SIFT. Though Harris finds the least matches, it still achieves the best performance on Precision excluding Leuven and Wall. The proposed method achieves the best performance as shown in Figure 15c. PCA-SIFT achieves four 100%, and SIFT achieves seven 100%. The recall performances are shown in Figure 15d, and it can be found that the proposed method achieves better performance than the other compared methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **Bark** & **Bikes** & **Boat** & **Graf** & **Leuven** & **Trees** & **Ubc** & **Wall** \\ \hline Harris & 1 & 9 & 6 & 3 & 0 & 2 & 4 & 0 \\ SIFT & 6 & 74 & 5 & 15 & 25 & 12 & 19 & 1 \\ PCA-SIFT & 0 & 73 & 3 & 3 & 26 & 4 & 17 & 0 \\ Proposed & 19 & 98 & 25 & 28 & 35 & 24 & 26 & 16 \\ \hline \end{tabular}
\end{table} 
Table 2: Correct numbers in the matching test on the Oxford database. Figure 14: Matching results of Oxford database: (**a**) Harris results; (**b**) SIFT results; (**c**) PCA-SIFT results; (**d**) our results.