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We present an application of gaze tracking to image and video indexing, in the form of a model for selecting and weighting Regions of Interest (RoIs). Image/video indexing refers to the process of creating a synthetic representation of the media, for instance for retrieval purposes. It usually consists in labeling the media with semantic keywords describing its content. When automatized, this process is based on the analysis of visual features, which can be extracted either from the whole image or keyframe, or locally from regions. Since most of the times the whole image is not relevant for indexing (e.g. large flat regions with no specific semantic interpretation, blur regions, background regions that may not be relevant for retrieval purposes, and that should be filtered out), it would be preferable to concentrate the labeling process on specific RoIs that are considered representative of the scene, like the main subjects. The objective of the work presented here is to take advantage of natural human gaze information in order to define a human-centered Region of Interest selection and weighting technique in the context of media retrieval.

Human-centered Region Selection and Weighting for Image Retrieval