Impact of Saliency and Gaze Features on Visual Control
Souad Chaabouni, Fŕed́eric Precioso · 2019
Predicting user intent from gaze presents a challenging question for developing real-time interactive systems like interactive search engine, implicit annotations of large datasets or intelligent robot behavior. Indeed, solutions to annotate easily large sets of images while reducing the burden of annotators is a key aspect for current machine learning techniques. We propose in this paper to design an estimator of the user interest for a given visual content based on eye-tracker feature analysis. We revise existing gaze-based interest estimator, and analyze the impact of the intrinsic saliency of the content displayed for interest estimation. We first explore low-level saliency prediction and propose a new gaze and saliency interest estimator. Experimental results show the advantage of our method for the annotation task in a weakly supervised context. In partic- ular, we extend previous evaluation criteria on new experimental protocol displaying four images by frame as a first step towards "Google Image search-like" interfaces. Our Gaze and Saliency Inter-est Estimator (GSIE) reaches an overall accuracy of 83% in average of user interest prediction. If we consider the accuracy reached in a limited time, the GSIE is 70% in average within about 500ms and 80% in average within 1000ms. This result confirms our GSIE as an efficient real-time visual control solution.