Fig1. "Screenshot of the GaZIR interface. Relevance feedback gathered from outer rings influences the images retrieved for the inner rings, and the user can zoom in to reveal more rings."
Fig2. "Precision-recall and ROC curves for userindependent relevance prediction model. The predictions (solid line) are clearly above the baseline of random ranking (dash-dotted line), showing that relevance of images can be predicted from eye movements. The retrieval accuracy is also above the baseline provided by a naive model making a binary relevance judgement based on whether the image was viewed or not (dashed line), demonstrating the gain from more advanced gaze modeling."
Fig 3. "Retrieval performance in real user experiments. The bars indicate the proportion of relevant images shown during the search in six different search tasks for three different feedback methods. Explicit denotes the standard point-and-click feedback, predicted means implicit feedback inferred from gaze, and random is the baseline of providing random feedback. In all cases both actual feedback types outperform the baseline, but the relative performance of explicit and implicit feedback depends on the search task."- László Kozma, Arto Klami, and Samuel Kaski: GaZIR: Gaze-based Zooming Interface for Image Retrieval. To appear in Proceedings of 11th Conference on Multimodal Interfaces and The Sixth Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI), Boston, MA, USA, Novermber 2-6, 2009. (abstract, pdf)
