Stable local feature detection and representation is a fundamental
component of many image registration and object recognition
algorithms. Mikolajczyk and Schmid recently evaluated a variety
of approaches and identified the SIFT algorithm as being the most
resistant to common image deformations. This paper examines (and
improves upon) the local image descriptor used by SIFT. Like
SIFT, our descriptors encode the salient aspects of the image gradient
in the feature point's neighborhood; however, instead of using SIFT's
smoothed weighted histograms, we apply Principal Components Analysis
(PCA) to the normalized gradient patch. Our experiments
demonstrate that the PCA-based local descriptors are more distinctive,
more robust to image deformations, and more compact than the standard
SIFT representation. We also present results showing that using
these descriptors in an image retrieval application results in
increased accuracy and faster matching.