# Heterogeneous Image Matching via a Novel Feature Describing Model
## 4 Experiments  
In real scenes, images are often affected by changes of viewpoint, scale, illumination, and so on. We ran two main types of experiments to explore the difference between the traditional methods and the proposed method. The first types of experiments explore the robustness to effects caused by rotation and the addition of noise. The second types of experiments verify the efficiency of the proposed method to heterogeneous image matching. We collected several classical natural images and heterogeneous image pairs. Some transforms are applied to them: (1) additional Gaussian noise (SNR = 20 db); (2) rotation of \(7\pi/36\) followed by additional Gaussian noise; and (3) scaled 50% and rotated \(\pi/6\). The experiments are completed under Windows 7 with Matlab R2017b. To evaluate the performance, the proposed algorithm is compared with Harris, SIFT, and PCA-SIFT. For more credible and stable results, the first two experiments are repeated for 10 times, and the results are computed on average. In addition, according to the widely known work for features matching performance evaluation [5; 46], correct number, cost time per match (ms), precision, and recall are applied to evaluate the performance. The related definitions are stated as follows. The correspondence between two images can be computed as illustrated in [47]:  
\[ \begin{split} Precision&=\frac{Num_{correctmatches}}{Num _{correctmatches}+Num_{fastmatches}},\\ Recall&=\frac{Num_{correctmatches}}{Num_{correspondances}}. \end{split}  \tag{17}\]  
**Noised images matching test**. The four images (Pavilion, Monkey, Fruits, Elaine) are all added Gaussian noise (SNR=20 db) for their polluted versions. Then, the proposed method and the compared methods are all carried out on each pair (original and polluted). Matching results of one implementation are shown in Figure 6. The rows from top to bottom mean different image pairs, Pavilion, Monkey, Fruits and Elaine, corresponding to the horizontal axis in Figure 7. The columns from left to right mean different methods: Harris, SIFT, PCA-SIFT, and the proposed method. Harris finds the least matching number while SIFT and PCA-SIFT achieve better performance. More matching can be found by the proposed method than others in this test. It can be found that 91 matches in the Pavilion image pair while 100 matches in the Monkey image pair by the proposed method. In addition, 52 matches and 98 matches can be found independently in the Fruits image pair and Elaine image pair. More details of an accurate matching number can be found in Figure 7a. The cost time per match is shown in Figure 7b, and the proposed method achieves better performance than SIFT and PCA-SIFT. The bar value means the total cost time and the green part means the localization time. Precision and recall of each image pair are shown in Figure 7c,d. It can be found PCA-SIFT loses on precision but gains on recall compared to SIFT. The proposed method can achieve higher precision and recall than SIFT and PCA-SIFT, but roughly equivalent to Harris. **Rotated and noised images matching test**. The four images (Pavilion, Monkey, Fruits, Elaine) are all added Gaussian noise (SNR = 20 db) after rotation by \(\frac{\pi}{6}\). Then, the proposed method and the compared methods are all carried out on each pair. Matching results are shown in Figures 8 and 9. The columns mean different method results. [PERSON] achieves accuracy of about 95% on the Pavilion image pair, and it finds only one match in the Fruits image pair. Our method has an advantage on the correct matching number. The details of an accurate matching number can be found in Figure 9a. As shown in Figure 9a, [PERSON] finds the least matching number in each image pair. The proposed method finds the most matches in three of four image pairs. It can be found that there are 42 matches in the Pavilion image pair while 60 matches in the Monkey image pair. In addition, 23 matches and 41 matches can be found independently in the Fruits image pair and Elaine image pair.