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Spectral Hashing

Spectral Hashing

Spectral Hashing. Y. Weiss (Hebrew U.) A. Torralba (MIT) Rob Fergus (NYU). What does the world look like?. Motivation. High level image statistics. Object Recognition for large-scale search . Semantic Hashing. [Salakhutdinov & Hinton, 2007]. Query Image. Semantic Hash Function.

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1.95k views • 41 slides
Efficient Sketches for Earth-Mover Distance, with Applications

Efficient Sketches for Earth-Mover Distance, with Applications

Efficient Sketches for Earth-Mover Distance, with Applications. David Woodruff IBM Almaden. Joint work with Alexandr Andoni, Khanh Do Ba, and Piotr Indyk. (Planar) Earth-Mover Distance. For multisets A , B of points in [ ?] 2 , | A |=| B |= N ,

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272 views • 20 slides
What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

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260 views • 1 slides
What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

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175 views • 1 slides
VLSH: Voronoi-based Locality Sensitive Hashing

VLSH: Voronoi-based Locality Sensitive Hashing

VLSH: Voronoi-based Locality Sensitive Hashing. Sung- eui Yoon Authors: Lin Loi , Jae- Pil Heo , Junghwan Lee, and Sung- Eui Yoon KAIST. http://sglab.kaist.ac.kr/VLSH/. Main Goals. Provide efficient nearest neighbor search for various motion planners, PRM and RRT

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211 views • 0 slides

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