# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 4 Experimental Results
### Keypoint Matching Accuracy
#### 4.1.3 Performance Evaluation  
Figure 8 shows the resultant considered method performances, where each datapoint was the average for 3-6 continuously varying test images, and we increased DR 0-1 with 0.05 interval. MIFT achieved significantly superior performance than the other SIFT based algorithms for all deformations, with general performance order being MIFT, MDGHM-SIFT, SIFT, MDGHM-SURF, SURF and PCA-SIFT algorithms for most deformation cases. Thus, MDGHM had a positive effect on feature representation ability for image information.  
Figure 7: Example dataset reference (left) and distorted (right) image pairs: (**a**) scale; (**b**) rotation; (**c**) viewpoint; (**d**) blur; (**e**) JPEG compression; and (**f**) illumination.  
Table 2 shows performance metric outcomes. MIFT F-score increased approximately 32.5%, 21.4%, 41.9%, 14.9%, 20.1%, and 57.8% for scale, rotation, viewpoint, blur, JPEG compression, and illumination distortions, respectively, compared with conventional SIFT. The proposed MIFT method exhibited significantly superior performance compared with the other SIFT based algorithms for F-score. Thus, MIFT was the most effective method tested, which attributed to employing the MDGHM based pyramid and MDGHM based feature description. The MDGHM pyramid generated more distinctive keypoints from scale space extrema detection, producing stronger histogram peaks during MIFT orientation assignment. Therefore, MIFT extracted more distinguishable final keypoints and, hence, achieved superior matching accuracy.  
Figure 8: Comparison between the proposed MIFT and five SIFT based algorithms (as defined in Table 1) for (**a**) scale; (**b**) rotation; (**c**) viewpoint; (**d**) blur; (**e**) JPEG compression; and (**f**) illumination distortion.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Deformation/Metric**} & \multicolumn{3}{c}{**SIFT**} & \multicolumn{3}{c}{**PCA-SIFT**} & \multicolumn{3}{c}{**MDGHM-SIFT**} & \multicolumn{3}{c}{**MDGHM-SURF**} & \multicolumn{3}{c}{**MSIFT**} \\ \cline{2-13}  & **Recall** & **Precision** & **F-score** & **Recall** & **Precision** & **F-score** & **Recall** & **Precision** & **F-score** & **Recall** & **Precision** & **F-score** & **Recall** & **Precision** & **F-score** \\ \hline
**Scale** & 0.283 & 0.284 & 0.283 & 0.095 & 0.095 & 0.095 & 0.037 & 0.307 & 0.307 & 0.241 & 0.243 & 0.242 & 0.375 & 0.376 & 0.375 \\ \hline
**Rotation** & 0.628 & 0.633 & 0.630 & 0.534 & 0.539 & 0.537 & 0.700 & 0.707 & 0.703 & 0.615 & 0.618 & 0.417 & 0.765 & 0.766 & 0.765 \\ \hline
**Viewpoint** & 0.436 & 0.439 & 0.437 & 0.322 & 0.287 & 0.303 & 0.541 & 0.546 & 0.544 & 0.511 & 0.518 & 0.516 & 0.620 & 0.621 & 0.620 \\ \hline
**Blar** & 0.659 & 0.659 & 0.659 & 0.511 & 0.512 & 0.511 & 0.685 & 0.685 & 0.685 & 0.670 & 0.672 & 0.671 & 0.754 & 0.759 & 0.757 \\ \hline
**Compression** & 0.656 & 0.656 & 0.656 & 0.641 & 0.641 & 0.641 & 0.710 & 0.710 & 0.710 & 0.699 & 0.700 & 0.700 & 0.787 & 0.790 & 0.788 \\ \hline
**Illumination** & 0.512 & 0.523 & 0.517 & 0.384 & 0.381 & 0.383 & 0.616 & 0.620 & 0.618 & 0.546 & 0.550 & 0.548 & 0.815 & 0.817 & 0.816 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics.