is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus

next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multichannel non-linear SVMs. The method is shown to improve state

the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step

The Laplacian pyramid (LP) is one of the earliest examples of multiscale representation of visual data. It is well known that an LP is overcomplete or redundant by construction, and has lower compression efficiency compared to critical representations such as wavelets and subband coding

coding performance when compared to the standard Laplacian pyramid. Moreover, our pyramid does not require biorthogonal filters as they should be used for the frame reconstruction of the Laplacian pyramid. Therefore, low-pass filters can be chosen that suppress aliasing in the lowresolution images

coding performance when compared to the standard Laplacian pyramid. Moreover, our pyramid does not require biorthogonal filters as they should be used for the frame reconstruction of the Laplacian pyramid. Therefore, low-pass filters can be chosen that suppress aliasing in the lowresolution images

is noisy. Thus, the smoothing of the tar-get function with respect to the intrinsic, low-dimensional representation that describes the geometric structure of the examined data is a challenging task. In this paper we propose Auto-adaptive Laplacian Pyra-mids (ALP), an extension of the standard Laplacian

datasets representation and learning using diffusion maps and

The Laplacian pyramid (LP) is a useful tool for obtaining spa-tially scalable representations of visual signals such as image and video. However, the LP is overcomplete or redundant and has lower compression efficiency compared to critical representations such as wavelets and subband coding

to the best results (based on quadratic SVM), while being 15 × faster. In these experiments our approximate IKSVM is up to 2000 × faster than a standard implementation and requires 200 × less memory. Finally we show that a 50 × speedup is possible using approximate IKSVM based on spatial pyramid features