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 recognized wedding events, e.g., the couple’s wedding kiss. Our goal is to develop an automatic tool for users to efficiently organize, search, and retrieve his/her treasured wedding memories. Furthermore, the event descriptions could benefit and complement the current research in semantic video understanding. Technically, three kinds of event features, i.e., the speech/music discriminator, flashlight detector, and bride indicator, are exploited to build statistical models for each wedding event. 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 rich set of wedding videos, and the results demonstrate the effectiveness of our approach.

We present an interactive and multi-level abstraction framework for user-generated video (UGV) summarisation, allowing a user the flexibility to select a summarisation criterion out of a number of methods provided by the system. First, a given raw video is segmented into shots, and each shot is further decomposed into sub-shots in line with the change in dominant camera motion. Secondly, principal component analysis (PCA) is applied to the colour representation of the collection of sub-shots, and a content map is created using the first few components. Each sub-shot is represented with a “footprint ” on the content map, which reveals its content significance (coverage) and the most dynamic segment. The final stage of abstraction is devised in a user-assisted manner whereby a user is able to specify a desired summary length, with options to interactively perform abstraction at different granularity of visual comprehension. The results obtained show the potential benefit in significantly alleviating the burden of laborious user intervention associated with conventional video editing/browsing.