Editing Scale-invariant feature transform (section)

== Comparison of SIFT features with other local features ==
There has been an extensive study done on the performance evaluation of different local descriptors, including SIFT, using a range of detectors.<ref name=Mikolajczyk2005 /> The main results are summarized below:

* SIFT and SIFT-like [[GLOH]] features exhibit the highest matching accuracies ([[Recall (information retrieval)|recall]] rates) for an affine transformation of 50 degrees. After this transformation limit, results start to become unreliable.
* Distinctiveness of descriptors is measured by summing the eigenvalues of the descriptors, obtained by the [[Principal components analysis]] of the descriptors normalized by their variance. This corresponds to the amount of variance captured by different descriptors, therefore, to their distinctiveness. PCA-SIFT (Principal Components Analysis applied to SIFT descriptors), GLOH and SIFT features give the highest values.
* SIFT-based descriptors outperform other contemporary local descriptors on both textured and structured scenes, with the difference in performance larger on the textured scene.
* For scale changes in the range 2–2.5 and image rotations in the range 30 to 45 degrees, SIFT and SIFT-based descriptors again outperform other contemporary local descriptors with both textured and structured scene content.
* Introduction of blur affects all local descriptors, especially those based on edges, like [[shape context]], because edges disappear in the case of a strong blur. But GLOH, PCA-SIFT and SIFT still performed better than the others. This is also true for evaluation in the case of illumination changes.

The evaluations carried out suggests strongly that SIFT-based descriptors, which are region-based, are the most robust and distinctive, and are therefore best suited for feature matching. However, most recent feature descriptors such as [[Speeded up robust features|SURF]] have not been evaluated in this study.

SURF has later been shown to have similar performance to SIFT, while at the same time being much faster.<ref name="SURF" /> Other studies conclude that when speed is not critical, SIFT outperforms SURF.<ref name="Lin15JMIV" /><ref name="SURFvsSIFT" /> Specifically, disregarding discretization effects the pure image descriptor in SIFT is significantly better than the pure image descriptor in SURF, whereas the scale-space extrema of the determinant of the Hessian underlying the pure interest point detector in SURF constitute significantly better interest points compared to the scale-space extrema of the Laplacian to which the interest point detector in SIFT constitutes a numerical approximation.<ref name="Lin15JMIV" />

The performance of image matching by SIFT descriptors can be improved in the sense of achieving higher efficiency scores and lower 1-[[Precision (information retrieval)|precision]] scores by replacing the scale-space extrema of the difference-of-Gaussians operator in original SIFT by scale-space extrema of the determinant of the Hessian, or more generally considering a more general family of generalized scale-space interest points.<ref name="Lin15JMIV" />

Recently, a slight variation of the descriptor employing an irregular histogram grid has been proposed that significantly improves its performance.<ref name="IrrGrid" /> Instead of using a 4×4 grid of histogram bins, all bins extend to the center of the feature. This improves the descriptor's robustness to scale changes.

The SIFT-Rank<ref name="Toews2009" /> descriptor was shown to improve the performance of the standard SIFT descriptor for affine feature matching. A SIFT-Rank descriptor is generated from a standard SIFT descriptor, by setting each histogram bin to its rank in a sorted array of bins. The Euclidean distance between SIFT-Rank descriptors is invariant to arbitrary monotonic changes in histogram bin values, and is related to [[Spearman's rank correlation coefficient]].