Automatic Generation of Video Summaries for Historical Films

A video summary is a sequence of video clips extracted from a longer video. Much shorter than the original, the summary preserves its essential messages. In the project ECHO (European Chronicles On-line) a system was developed to store and manage large collections of historical films for the preservation of cultural heritage. At the University of Mannheim we have developed the video summarization component of the ECHO system. In this paper we discuss the particular challenges the historical film material poses, and how we have designed new video processing algorithms and modified existing ones to cope with noisy black-and-white films. We also report empirical results from the use of our summarization tool at the four major European national video archives

Sequential sparsification for change detection

This paper presents a general method for segmenting a vector valued sequence into an unknown number of subsequences where all data points from a subsequence can be represented with the same affine parametric model. The idea is to cluster the data into the minimum number of such subsequences which, as we show, can be cast as a sparse signal recovery problem by exploiting the temporal correlation between consecutive data points. We try to maximize the sparsity (i.e. the number of zero elements) of the first order differences of the sequence of parameter vectors. Each non-zero element in the first order difference sequence corresponds to a change. A weighted l1 norm based convex approximation is adopted to solve the change detection problem. We apply the proposed method to video segmentation and temporal segmentation of dynamic textures. 1

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Automatic Movie Abstracting

Presented is an algorithm for automatic production of a video abstract of a feature film, similar to a movietrailer. It selects clips from the original movie based on detection of special events like dialogs, shots, explosions and text occurrences, and on general action indicators applied to scenes. These clips are then assembled to form a video trailer using a model of editing. Additional clips, audio pieces, images and text, which are also retrieved from the original video for their content, are added to produce a multimedia abstract. The collection of multime dia objects is presented on an HTML-page

A hierarchical multi-modal approach to story segmentation in news video

Ph.DDOCTOR OF PHILOSOPH