PCA-SIFT Details

PCA-SIFT was introduced as an improvement for SIFT. The implementation only differs in the 4th step (keypoint descriptor). PCA-SIFT uses Principal Component Analysis (PCA) instead of a histogram to normalize the gradient patch. Hence, the feature vector is significantly smaller than the standard SIFT feature vector.

It differs from the original SIFT implementation in its forth stage. By reducing the 128 element of the original SIFT using PCA, results in a significant space benefit.

Another difference is the input vector is created by concatenating the horizontal and vertical gradient maps for 41x41 patch centered at the keypoint. Producing 2x39x39 = 3042 vector elements. The fewer components of PCA-SIFT requires less storage therefore, resulting in faster matching. The dimensionality of this feature space is arbitrary chosen as n=20, which results in a significant space benefit.

TO DO: add more detail