49. IRPR is a common measure in database retrieval
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Hinweis der Redaktion
Google has their own format –SketchUp (SU) and has been investing a lot of effort in 3D technologies. More examples from google include – google earth, google sketch-up and O3D platform for browser 3D display
Other manifold learning techniques – PCA (+variants), Kernel PCA, LLE (locally linear embedding), ISOMAP, Hessian LLE, LaplacianEigenMaps and many more…
Reference[9]
Reference [2]
See [1,6] for diffusion maps equations and [3] for graph
Ignore λmax=1 since it is a trivial eigenvalue of the markov transition matrix and corresponds to the stationary distribution of the markov chain at t=∞Cut is promised to conform with min-cut max-flow algorithmSee reference [6] for a thorough description of diffusion maps
We see that it is possible to reconstruct the manifold using just a single eigenvalue. Cuts can be made on the eigenvectors that represent the manifold (in this case, the second or the third eigenvectors corresponding to the second or third largest eigenvalue) – these cuts are meaningful since they are taking into account the geometric distribution of the original points. We see that diffusion maps approximate the Fourier series over the circle as the sine and cosine functions are the solution of the differential equation f’’=-f
Animated objects can be more sensitive to mesh simplification algorithms than CAD models.
Haar wavelet with 3 levels of decomposition to the Stanford Bunny image. By applying a threshold in the wavelet domain we can efficiently find similar images. The threshold is a very efficient way to remove noise. The high value coefficients correspond to edges at various scales. Collecting the high value coefficients to create a feature vector would ensure a good representation of the image. For example, check the lossy compression algorithm JPEG-2000.
We take 4 levels of decomposition since there is no real advantage in taking more decomposition levels and the computational burden is heavy. This result is specific to Princeton’s database and can change when dealing with different databases.
Take inverse of FDR to avoid numeric problems of overflow. Select model with minimum within cluster scattering and maximum within cluster scattering
Can do training on the entire database…See [5] for database
Chair – sparse structurePlane – smooth surface with local singularityKangaroo – smooth surfaceFlower – combines smooth surface with local singularity