A model for image patch-based algorithms
An empirical study of the domain of patch-based learning algorithms for image and video processing is conducted. As patch-based algorithms are commonly used, knowledge of the properties of fixed size image patches would prove particularly useful and interesting. We are concerned with investigating the overall distribution of vectorized patches of general images. A multivariate distribution model is proposed and analyzed using various techniques, which include univariate histograms and modified k-nearest neighbors. The model is verified and an application using the distribution model is introduced and compared.