25 research outputs found
A Neural Algorithm of Artistic Style
In fine art, especially painting, humans have mastered the skill to create
unique visual experiences through composing a complex interplay between the
content and style of an image. Thus far the algorithmic basis of this process
is unknown and there exists no artificial system with similar capabilities.
However, in other key areas of visual perception such as object and face
recognition near-human performance was recently demonstrated by a class of
biologically inspired vision models called Deep Neural Networks. Here we
introduce an artificial system based on a Deep Neural Network that creates
artistic images of high perceptual quality. The system uses neural
representations to separate and recombine content and style of arbitrary
images, providing a neural algorithm for the creation of artistic images.
Moreover, in light of the striking similarities between performance-optimised
artificial neural networks and biological vision, our work offers a path
forward to an algorithmic understanding of how humans create and perceive
artistic imagery
A framework for high dimensional data reduction in the microarray domain
Microarray analysis and visualization is very helpful for biologists and clinicians to understand gene expression in cells and to facilitate diagnosis and treatment of patients. However, a typical microarray dataset has thousands of features and a very small number of observations. This very high dimensional data has a massive amount of information which often contains some noise, non-useful information and small number of relevant features for disease or genotype. This paper proposes a framework for very high dimensional data reduction based on three technologies: feature selection, linear dimensionality reduction and non-linear dimensionality reduction. In this paper, feature selection based on mutual information will be proposed for filtering features and selecting the most relevant features with the minimum redundancy. A kernel linear dimensionality reduction method is also used to extract the latent variables from a high dimensional data set. In addition, a non-linear dimensionality reduction based on local linear embedding is used to reduce the dimension and visualize the data. Experimental results are presented to show the outputs of each step and the efficiency of this framework. © 2010 IEEE
Video from nearly still: An application to low frame-rate gait recognition
In this paper, we propose a temporal super resolution ap-proach for quasi-periodic image sequence such as human gait. The proposed method effectively combines example-based and reconstruction-based temporal super resolution approaches. A periodic image sequence is expressed as a manifold parameterized by a phase and a standard mani-fold is learned from multiple high frame-rate sequences in the training stage. In the test stage, an initial phase for each frame of an input low frame-rate image sequence is estimated based on the standard manifold at first, and the manifold reconstruction and the phase estimation are then iterated to generate better high frame-rate images in the energy minimization framework that ensures the fitness to both the input images and the standard manifold. The pro-posed method is applied to low frame-rate gait recognition and experiments with real data of 100 subjects demonstrate a significant improvement by the proposed method, particu-larly for quite low frame-rate videos (e.g., 1 fps). 1
A Spectral Theory for Tensors
In this paper we propose a general spectral theory for tensors. Our proposed
factorization decomposes a tensor into a product of orthogonal and scaling
tensors. At the same time, our factorization yields an expansion of a tensor as
a summation of outer products of lower order tensors . Our proposed
factorization shows the relationship between the eigen-objects and the
generalised characteristic polynomials. Our framework is based on a consistent
multilinear algebra which explains how to generalise the notion of matrix
hermicity, matrix transpose, and most importantly the notion of orthogonality.
Our proposed factorization for a tensor in terms of lower order tensors can be
recursively applied so as to naturally induces a spectral hierarchy for
tensors.Comment: The paper is an updated version of an earlier versio