Co-saliency detection is a newly emerging and rapidly growing research area in the
computer vision community. As a novel branch of visual saliency, co-saliency detection …

This paper proposes a superpixel-based spatiotemporal saliency model for saliency
detection in videos. Based on the superpixel representation of video frames, motion …

Systems based on bag-of-words models from image features collected at maxima of sparse
interest point operators have been used successfully for both computer visual object and …

General dynamic scenes involve multiple rigid and flexible objects, with relative and
common motion, camera induced or not. The complexity of the motion events together with …

Over the past few years, deep neural networks (DNNs) have exhibited great success in
predicting the saliency of images. However, there are few works that apply DNNs to predict …

Datasets of images or videos annotated with eye tracking data constitute important ground
truth for studies on saliency models, which have applications in quality assessment and …

Predicting visual attention facilitates an adaptive virtual museum environment and provides
a context-aware and interactive user experience. Explorations toward development of a …

Co-saliency detection is a newly emerging and rapidly growing research area in computer
vision community. As a novel branch of visual saliency, co-saliency detection refers to the …

This chapter clarifies the concept and the usage of visual attention in multimedia
applications. Visual attention modeling has been the focus of many research efforts in the …

Existing saliency prediction methods focus on exploring a universal saliency model for
natural images, relatively few on advertising images which typically consists of both textual …