PCA-SIFT is a modification of SIFT that uses principal component analysis (PCA) to build more distinctive local image descriptors. It constructs a projection matrix from a large set of image patches, then projects each keypoint descriptor through this matrix to a compact vector of the top n principal components. This provides a more discriminative representation than SIFT while reducing descriptor dimensionality, leading to improved matching accuracy and efficiency. Evaluation on controlled transformation and graffiti datasets shows PCA-SIFT achieves higher recall rates at equivalent or lower false positive rates compared to SIFT.

1. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors by Yan Ke and Rahul Sukthankar Presentation by Guy Tannenbaum

11. Results – Controlled transformation

13. Results3 – running time