The priority curve algorithm for video summarization

In this paper, we introduce the concept of a priority curve associated with a video. We then provide an algorithm that can use the priority curve to create a summary (of a desired length) of any video. The summary thus created exhibits nice continuity properties and also avoids repetition. We have implemented the priority curve algorithm (PriCA) and compared it with other summarization algorithms in the literature with respect to both performance and the output quality. The quality of summaries was evaluated by a group of 200 students in Naples, Italy, who watched soccer videos. We show that PriCA is faster than existing algorithms and also produces better quality summaries. We also briefly describe a soccer video summarization system we have built on using the PriCA architecture and various (classical) image processing algorithms.     

Smart VideoText: a video data model based on conceptual graphs

An intelligent annotation-based video data model called Smart VideoText is introduced. It utilizes the conceptual graph knowledge representation formalism to capture the semantic associations among the concepts described in text annotations of video data. The aim is to achieve more effective query, retrieval, and browsing capabilities based on the semantic content of video data. Finally, a generic and modular video database architecture based on the Smart VideoText data model is described.