PCA-SIFT: A More Distinctive Representation for Local Image
Descriptors
by Yan Ke and Rahul Sukthankar
Abstract:
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.
Y. Ke and R. Sukthankar, Computer Vision and Pattern Recognition,
2004. [PDF 670KB]
PCA-SIFT (calculates representation only) source code: pcasift-0.91nd.tar.gz (700KB).
You'll need the netpbm development libraries to compile this code.
If you want to train PCA on your own patches, here's the Matlab code for it.
training-matlab.tgz
Keypoint detection as Linux binary and modified matching program as source
code from David Lowe. Works on PCA-SIFT keys and
Lowe's SIFT keys. Includes example images for matching. mod_lowe_demoV2.tar.gz (430KB)
Source code for PCA-SIFT with integrated Difference of Gaussian
(DoG) interest point detector can be obtained for research purposes.
However, before you ask me for it, please note that the PCA-SIFT code
supplied above is sufficient for most cases. If you are simply looking
for the DoG interest point detector, there are many other sources
online.
Dataset used in the experiments:
testimages.tgz (550KB) for recall-precision
curves
and objects.tgz (9 MB) for image retrieval.
Links:
Parts based image retrieval
David Lowe's Keypoints
Page maintained by Yan Ke < y k e @ c m u . e d u >
April 2004