Previous work on predicting the target of visual search from human fixations only considered
closed-world settings in which training labels are available and predictions are performed …

Extensive use of digital photographic devices has resulted in large volumes of digital images
being acquired and stored in databases. Whether it is for scientific research, medical or …

Recommender systems can support everyday digital tasks by retrieving and recommending
useful information contextually. This is becoming increasingly relevant in services and …

Computational prediction of facial aesthetics has attracted ever-increasing research focus,
which has wide range of prospects in multimedia applications. The key challenge lies in …

Content-Based Medical Image Retrieval (CBMIR) systems primarily aid in extracting useful
information from a large set of medical images. CBMIR procedures are useful in …

Relevance feedback (RF) is an effective approach to bridge the gap between low-level
visual features and high-level semantic meanings in content-based image retrieval (CBIR) …

Estimating three-dimensional (3D) human eye gaze by capturing a single eye image without
active illumination is challenging. Although the elliptical iris shape provides a useful cue …

Predicting the target of visual search from human gaze data is a challenging problem. In
contrast to previous work that focused on predicting specific instances of search targets, we …

Predicting the target of visual search from human eye fixations (gaze) is a difficult problem
with many applications, eg in human-computer interaction. While previous work has focused …

Generally, medical content-based image retrieval (CBIR) systems select low-level visual
features as image descriptors. However, these descriptors fail to provide clues for …