# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.4 Descriptor Matching Using Different Matching Strategies  
Table 5 presents a comparison between Reg-SIFT and state of art descriptors using different feature matching strategies. The matching strategies used are Triangle Area Representation (TAR) [37], Fast Sample Consensus (FSC) [38], Locally Linear Transforming (LLT) [39], Particle Swarm Optimization Sample Consensus (PSOAC) [40], and Guided Locality Preserving Matching (GLPM) [41]. The ratio test based matching, which is used in [10,42] can also be used but it normally works well only on SIFT descriptors.  
Figure 5: Image matching results achieved on a scene of MSD with SIFT, MFD, HoDM and Reg-SIFT. SIFT detector is used. [PERSON] and blue lines connect correct and false descriptor matches, respectively. Each sub-figure shows a VS (**left**) and a LWIR (**right**) image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Descriptor** & **BFDM** & **TAR**[37] & **FSC**[38] & **LLT**[39] & **PSOSC**[40] & **GLPM**[41] \\ \hline RGB-NIR & SIFT & 34 & 30 & 39 & 40 & 47 & 51 \\ SURF & 19 & 22 & 26 & 26 & 31 & 32 \\ ORB & 36 & 38 & 39 & 43 & 50 & 51 \\ BRISK & 32 & 29 & 34 & 37 & 37 & 45 \\ GOM-SIFT & 35 & 37 & 39 & 43 & 49 & 55 \\ OR-SIFT & 35 & 36 & 38 & 40 & 43 & 47 \\ EOH & 25 & 17 & 26 & 31 & 42 & 49 \\ LBPG & 32 & 38 & 42 & 51 & 58 & 59 \\ LS-SIFT & 37 & 40 & 42 & 46 & 55 & 62 \\ DE-SIFT & 39 & 43 & 46 & 50 & 57 & 65 \\ NG-SIFT & 38 & 46 & 51 & 62 & 65 & 69 \\ MN-SIFT & 46 & 53 & 59 & 68 & 70 & 75 \\ LS5 & 31 & 29 & 33 & 37 & 37 & 41 \\ VAR-EOH & 24 & 27 & 28 & 33 & 41 & 44 \\ LSSF & 32 & 28 & 37 & 39 & 40 & 45 \\ MFD & 43 & 32 & 39 & 48 & 53 & 59 \\ HoDM & 45 & 46 & 52 & 59 & 64 & 69 \\ Proposed & **60** & **61** & **65** & **73** & **76** & **83** \\ \hline MSD & SIFT & 3 & 4 & 6 & 8 & 8 & 11 \\ SURF & 1 & 2 & 4 & 7 & 9 & 9 \\ ORB & 1 & 2 & 5 & 6 & 8 & 10 \\ BRISK & 1 & 1 & 4 & 8 & 10 & 12 \\ GOM-SIFT & 4 & 7 & 8 & 10 & 13 & 18 \\ OR-SIFT & 4 & 5 & 7 & 9 & 12 & 17 \\ EGU & 3 & 2 & 5 & 10 & 12 & 15 \\ LBPG & 2 & 2 & 6 & 8 & 14 & 18 \\ LS-SIFT & 2 & 1 & 7 & 13 & 19 & 23 \\ DE-SIFT & 2 & 6 & 9 & 15 & 22 & 25 \\ NG-SIFT & 5 & 10 & 13 & 19 & 26 & 30 \\ MN-SIFT & 6 & 18 & 23 & 28 & 35 & 38 \\ LSS & 3 & 2 & 7 & 7 & 10 & 13 \\ VAR-EOH & 2 & 2 & 4 & 6 & 8 & 10 \\ LSSF & 7 & 18 & 25 & 34 & 35 & 40 \\ MFD & 11 & 22 & 30 & 38 & 39 & 43 \\ HoDM & 16 & 25 & 29 & 37 & 43 & 47 \\ Proposed & **27** & **33** & **42** & **49** & **53** & **58** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average matching scores (%) achieved by different descriptors using different feature matching strategies. The Scale-Invariant-Feature transform (SIFT) detector is used for feature point detection.  
Figure 6: **(a)** Average matching and **(b)** precision scores and precision scores achieved on MSD and RGB-NIR datasets with proposed Reg-SIFT (MLPR) and other descriptors.  
Similarly, Table 6 shows average precision score (%) based comparison between Reg-SIFT and others using different feature matching strategies, respectively. Both comparisons show that the performance of descriptors varies with respect to feature matching strategies and Reg-SIFT (MLPR) compared to others, works really well with all of them and outperforms all other descriptors.