Patent ID: 7991230

Claim:
A method, comprising: learning modeling parameters from a set of training images, the learning employing a pseudo maximum likelihood estimation that captures spatial dependencies in the set of training images to optimize the modeling parameters; dividing an image stored in a memory into overlapping blocks of the same size; modeling, by a processor configured with executable instructions and based on one or more selected attributes of pixels in each block, a micro-structure of each block in the image as a Markov Random Field using the modeling parameters; automatically designing a series of micro-patterns that corresponds to the micro-structure of each block, the series of micro-patterns capturing spatial correlations of the pixels in each block; calculating an occurrence probability of micro-patterns in each block to form a local fitness sequence of each block; and for each block, applying a Modified Fast Fourier Transform (MFFT) to the respective local fitness sequences to extract block-level features; and concatenating the block-level features from each block to represent the image, wherein the concatenated block-level features comprise a single feature vector representing: a spatial correlation of the image on a pixel level as captured by the series of micro-patterns designed for each block; a regional fitness of the image on a block level based on the respective local fitness sequences calculated for each block; and a description of the image on a global level that is a product of concatenating the block-level features.