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PCA-SIFT

swMATH ID:	4592
Software Authors:	Y. Ke and R. Sukthankar
Description:	PCA-SIFT: A More Distinctive Representation for Local Image Descriptors 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.
Homepage:	http://www.cs.cmu.edu/~yke/pcasift/
Related Software:	SIFT; SURF; ASIFT; BRISK; PASCAL VOC; FREAK; KAZE Features; Vlfeat; COIL-100; SIFER; PRMLT; SBA; LIBSVM; EMD; Key.Net; L2-Net; MatchNet; HPatches; LMNN; MonoSLAM
Cited in:	53 Documents

all top 5

all top 5

12	Pattern Recognition
7	Journal of Mathematical Imaging and Vision
6	International Journal of Computer Vision
5	Mathematical Problems in Engineering
2	Applied Mathematics and Computation
2	PAA. Pattern Analysis and Applications
2	SIAM Journal on Imaging Sciences
1	International Journal of Systems Science
1	Inverse Problems
1	Information Sciences
1	Computational Mathematics and Modeling
1	Computational Statistics and Data Analysis
1	Complexity
1	Journal of Machine Learning Research (JMLR)
1	Acta Numerica
1	Mathematical Biosciences and Engineering
1	Science in China. Series F
1	Advances in Data Analysis and Classification. ADAC
1	EURASIP Journal on Advances in Signal Processing
1	Foundations and Trends in Computer Graphics and Vision
1	Foundations and Trends in Signal Processing

all top 5

43	Computer science (68-XX)
17	Information and communication theory, circuits (94-XX)
8	Statistics (62-XX)
3	Numerical analysis (65-XX)
1	Combinatorics (05-XX)
1	Harmonic analysis on Euclidean spaces (42-XX)
1	Abstract harmonic analysis (43-XX)
1	Probability theory and stochastic processes (60-XX)
1	Operations research, mathematical programming (90-XX)