This paper presents a human-centered picture slideshow system for mobile users. In contrast to conventional ROIs (region-of-interest) detection based systems, we provide mobile users the freedom of personalizing ROIs in a convenient and effective way. Here, we import a simple human interaction, i.e., only a single click, to give a hint for users' ROIs. First, local saliency map (LSM) is generated, which considers not only multi-scale contrast, but also the self-correlation measure and central effect. Then a local fuzzy growing method is adopted to extract ROIs automatically based on LSM. Extensive experiments and user studies show the encouraging performance of the proposed system.

This paper introduces a system for providing on-demand sports video to mobile devices, which has two main contributions. First, we construct an infrastructure for extracting and delivering the highlights instead of the whole sport videos to mobile clients, which can significantly reduce the bandwidth consumption. Second, we design an advanced UI for the mobile clients to effectively browse and interact with the video highlights. To validate the practicality and effectiveness of this system, we conduct the experiments on several real soccer videos. The results demonstrated that more than 65% of bandwidth consumption could be reduced. Moreover, the initial user study results show that the mobile users could interact effectively with the interface to seek or navigate sports videos.

In this paper, we present a novel framework to customize multimedia messages for mobile users. The goal is to generate a video message from a series of pictures. The framework includes visual attention view detection, image grouping, image ranking, and slideshow generation. Considering the limitation of mobile device, we use a simple color feature based attention model to detect interesting regions of the images. We group the images, and rank them based on the attention view similarities. Finally a human perception based slideshow is designed to keep the mobile users' eye on attention regions efficiently. In addition, a short music is selected to match the video message. Extensive experiments and user studies show the promising performance of the proposed system.

In this paper, we present a novel framework that serves adaptive sports video to mobile users. Our framework combines contentbased sports highlights extraction and quality-domain video compression technologies in video server, capable of reducing wireless bandwidth consumption more effectively. We develop a robust replay-based highlights extraction method, and propose a content-based video streaming coding scheme to handle the problems of bandwidth and capacity of computation. To validate the practicality and effectiveness of our system, we conduct the experiments on several real soccer videos. The experimental results demonstrate the robustness of our highlights extraction method and more than 77.5% of the bandwidth consumption could be reduced.