Lab Meeting, December 19, 2013 (Yen-Ting): Deformable Spatial Pyramid Matching for Fast Dense Correspondences

Title: Deformable Spatial Pyramid Matching for Fast Dense Correspondences

Authors: Jaechul Kim, Ce Liu, Fei Sha and Kristen Grauman

Abstract: We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents—ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the “deformable” aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on LabelMe and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and PatchMatch [2]), both in terms of accuracy and run time.

P.S.
[2] C. Barnes, E. Shechtman, D. Goldman, and A. Finkelstein. The Generalized PatchMatch Correspondence Algorithm. In ECCV, 2010.
[15] C. Liu, J. Yuen, and A. Torralba. SIFT Flow: Dense Correspondence across Different Scenes and Its Applications. PAMI, 33(5), 2011.

From: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013

Link: Click here