Guiding human gaze with convolutional neural networks
This addresses the challenge of guiding human attention in digital images, which is incremental as it builds on existing fixation prediction models.
The paper tackled the problem of manipulating images to control human fixation patterns, achieving an average increase of 43% and decrease of 22% in fixation probability on selected objects, though effectiveness varied with image content.
The eye fixation patterns of human observers are a fundamental indicator of the aspects of an image to which humans attend. Thus, manipulating fixation patterns to guide human attention is an exciting challenge in digital image processing. Here, we present a new model for manipulating images to change the distribution of human fixations in a controlled fashion. We use the state-of-the-art model for fixation prediction to train a convolutional neural network to transform images so that they satisfy a given fixation distribution. For network training, we carefully design a loss function to achieve a perceptual effect while preserving naturalness of the transformed images. Finally, we evaluate the success of our model by measuring human fixations for a set of manipulated images. On our test images we can in-/decrease the probability to fixate on selected objects on average by 43/22% but show that the effectiveness of the model depends on the semantic content of the manipulated images.