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PCA-SIFT: A More Distinctive Representation for Local Image Descriptors

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Yan Ke and Rahul Sukthankar Presentation by Guy Tannenbaum

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PCA-SIFT: A More Distinctive Representation for Local Image Descriptors by Yan Ke and Rahul Sukthankar Presentation by Guy Tannenbaum
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object]
Quick review of SIFT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PCA-SIFT: Basic idea ,[object Object]
PCA-SIFT: computing a projection matrix ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PCA-SIFT: computing a projection matrix ,[object Object],[object Object],[object Object],[object Object]
Dimension reduction through PCA ,[object Object]
Constructing PCA-SIFT descriptor ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results - Methodology ,[object Object],[object Object],[object Object],[object Object]
Results - Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results – Controlled transformation
Results 2 – Grafitti dataset ,[object Object]
Results3 – running time
Eigenspace construction ,[object Object]
Effect of PCA dimension ,[object Object],[object Object]
Summary ,[object Object],[object Object]
Credits ,[object Object],[object Object],[object Object]

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