Image understanding based on histogram of contrast

An image descriptor based on the orientation of contrasts is proposed to facilitate image understanding. A mathematical function that satisfies the heat equation is proposed to model the parameters of an ideal edge, including the width, contrast, offset, position, and orientation. A simple method can compute the contrast and width of canny edge features. Inspired by the histogram of oriented gradients method, an image representation is proposed based on a histogram of contrasts. The proposed method encodes the weighted orientation in a sub-window. The weighting is based on the contrast instead of the gradient magnitude. Our experiments showed that the new representation was more robust against noise compared with several state-of-the-art methods. Several sub-window resolutions were also considered, which resulted in a pyramid that incorporated the global and local context. A method based on Bayes’ theorem and empirical cumulative distribution function is proposed to predict the probability of correctness for the nearest neighbor classification. The annotation and class label of an image were retrieved using a nearest neighbor method or a support vector machine classifier. This method was tested using several benchmark datasets and toolboxes, which showed that it is suitable for image understanding tasks.