Learning the Relative Importance of Features in Image Data

In computational analysis in scientific domains, images are often compared based on their features, e.g., size, depth and other domain-specific aspects. Certain features may be more significant than others while comparing the images and drawing corresponding inferences for specific applications. Though domain experts may have subjective notions of similarity for comparison, they seldom have a distance function that ranks the image features based on their relative importance. We propose a method called features rank for learning such a distance function in order to capture the semantics of the images. We are given training samples with pairs of images and the extent of similarity identified for each pair. Using a guessed initial distance function. Features rank clusters the given images in levels. It then adjusts the distance junction based on the error between the clusters and training samples using heuristics proposed in this paper. The distance junction that gives the lowest error is the output. This contains the features ranked in the order most appropriate the domain. Features rank is evaluated with real image data from nanotechnology and bioinformatics. The results of our evaluation are presented in the paper.