Gaze-guided Image Classification for Reflecting Perceptual Class Ambiguity

Abstract

Despite advances in machine learning and deep neural networks, there is still a huge gap between machine and human image understanding. One of the causes is the annotation process used to label training images. In most image categorization tasks, there is a fundamental ambiguity between some image categories and the underlying class probability differs from very obvious cases to ambiguous ones. However, current machine learning systems and applications usually work with discrete annotation processes and the training labels do not reflect this ambiguity. To address this issue, we propose an new image annotation framework where labeling incorporates human gaze behavior. In this framework, gaze behavior is used to predict image labeling difficulty. The image classifier is then trained with sample weights defined by the predicted difficulty. We demonstrate our approach's effectiveness on four-class image classification tasks.