Predicting relevance of parts of an image

Description/Abstract

This report studies the task of inferring which parts of an image are relevant for the user viewing the image. The relevance is inferred from gaze trajectory of users viewing the images given a specific task. Novel computational models based on both Bayesian generative modeling and kernel methods are developed for inferring the regions of interest from raw fixation data, as well as from combination of eye movements and image content features.