Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Abstract
A major obstacle in photography is the presence of distracting elements that pull attention away from the main subject and clutter the composition. In this article, we present a new image-processing technique that reduces the salience of distracting regions. It is motivated by computational models of attention that predict that texture variation influences bottom-up attention mechanisms. Our method reduces the spatial variation of texture using power maps, high-order features describing local frequency content in an image. We show how modification of power maps results in powerful image de-emphasis. We validate our results using a user search experiment and eye tracking data.
