Publication at Faculty of Mathematics and Physics |
2014
Abstract
This paper deals with Information Retrieval from audio-visual recordings. Such recordings are often quite long and users may want to find the exact starting points of relevant passages they search for.
In Passage Retrieval, the recordings are automatically segmented into smaller parts, on which the standard retrieval techniques are applied. In this paper, we discuss various techniques for segmentation of audio-visual recordings and focus on machine learning approaches which decide on segment boundaries based on various features combined in a decision-tree model.
Our experiments are carried out on the data used for the Search and Hyperlinking Task and Similar Segments in Social Speech Task of the MediaEval Benchmark 2013.