bonddraw.blogg.se

• Analysis And Synthesis Sparse Representation Models For Machine Learning
• Analysis And Synthesis Sparse Representation Models For Mac X
• Analysis And Synthesis Sparse Representation Models For Machines

This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary.

Analysis And Synthesis Sparse Representation Models For Machine Learning

It is well known that natural images admit sparse representations by redundant dictionaries of basis functions such as Gabor-like wavelets. However, it is still an open question as to what the next layer of representational units above the layer of wavelets should be. We address this fundamental question by proposing a sparse FRAME (Filters, Random field, And Maximum Entropy) model for representing natural image patterns. Our sparse FRAME model is an inhomogeneous generalization of the original FRAME model.

Analysis And Synthesis Sparse Representation Models For Mac X

It is a non-stationary Markov random field model that reproduces the observed statistical properties of filter responses at a subset of selected locations, scales and orientations. Each sparse FRAME model is intended to represent an object pattern and can be considered a deformable template. The sparse FRAME model can be written as a shared sparse coding model, which motivates us to propose a twostage algorithm for learning the model. The first stage selects the subset of wavelets from the dictionary by a shared matching pursuit algorithm. The second stage then estimates the parameters of the model given the selected wavelets. Our experiments show that the sparse FRAME models are capable of representing a wide variety of object patterns in natural images and that the learned models are useful for object classification. Inhomogeneous FRAME 1.1 Model.

Learning sequence by Inhomogeneous FRAME. The size of the images are 70 × 70. We work with grey-level images in this work. A separate model is learned from each training set shown in Fig 1 (Top: Hummingbird. Bottom: Cat). Synthesized images generated in step t = 1, 4, 7, 10, 13, 20, 50, 100, 200, 300, 400, and 500.

Sparse FRAME 2.1 Model In model (1), the ( x,s,α) in x,s,α is over all the pixels x and all the scales s and orientations α. We call such a model the dense FRAME.

Analysis And Synthesis Sparse Representation Models For Machines

It is possible to sparsify the model by selecting only a small set of ( x,s,α) so that x,s,α is restricted to this selected subset, while such a selected subset of basis functions should be enough to interpret or reconstruct the training images. More explicitly, we can write the sparsified model as. The images are 100 x 100. The number of selected wavelets is 300. The first row are symbolic sketches of selected Gabor wavelets at different scales, where each selected Gabor wavelet is symbolized by a bar.