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2013 IEEE Conference on Computer Vision and Pattern Recognition (2004)
Washington, D.C., USA
June 27, 2004 to July 2, 2004
ISSN: 1063-6919
ISBN: 0-7695-2158-4
pp: 506-513
Yan Ke , Carnegie Mellon University
Rahul Sukthankar , Carnegie Mellon University and Intel Research Pittsburgh
Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] 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.
Yan Ke, Rahul Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 02, no. , pp. 506-513, 2004, doi:10.1109/CVPR.2004.183
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