Nowadays, travel has become a popular activity for people to relax their body and mind. Taking photos is then often an inevitable and frequent event during one's trip for recording the enjoyable experience. To help people to relive the wonderful travel experience they had recorded in photos, this paper presents a system, Photo Navigator, for enhancing the photo browsing experience by creating a new browsing style with a realistic feel to users as being into the scenes and taking a trip back in time to revisit the place. The proposed system is characterized by two main features. First, it better reveals the spatial relations among photos and offers a strong sense of space by taking users to fly into the scenes. Second, it is fully automatic and makes plausible for novice users to utilize the 3D technologies that are traditionally complex to manipulate. The proposed system is compared with two other photo browsing tools, ACDSee's photo slideshow and Microsoft's PhotoStory. User studies show that people would comparatively favor the browsing style we offer and appreciate the ease to create such a style.

Wedding is one of the most important ceremonies in our lives. It symbolizes the birth and creation of a new family. In this paper, we present a system for automatically segmenting a wedding ceremony video into a sequence of recognizable wedding events, e.g., the couple's wedding kiss. Our goal is to develop an automatic tool that helps users to efficiently organize, search, and retrieve his/her treasured wedding memories. Furthermore, the obtained event descriptions could benefit and complement the current research in semantic video understanding. Based on the knowledge of wedding customs, a set of audiovisual features, relating to the wedding contexts of speech/music types, applause activities, picture-taking activities, and leading roles, are exploited to build statistical models for each wedding event. Thirteen wedding events are then recognized by a hidden Markov model, which takes into account both the fitness of observed features and the temporal rationality of event ordering to improve the segmentation accuracy. We conducted experiments on a collection of wedding videos and the promising results demonstrate the effectiveness of our approach. Comparisons with conditional random fields show that the proposed approach is more effective in this application domain.