Labeling TV Stream Segments with Conditional Random Fields

Abstract

In this paper, we consider the issue of structuring large TV streams. More precisely, we focus on the labeling problem: once segments have been extracted from the stream, the problem is to automatically label them according to their type (eg. programs vs. commercial breaks). In the literature, several machine learning techniques have been proposed to solve this problem: Inductive Logic Programming, numeric classifiers like SVM or decision trees... In this paper, we assimilate the problem of labeling segments to the problem of labeling a sequence of data. We propose to use a very effective approach based on another classifier: the Conditional Random Fields (CRF), a tool which has proved useful to handle sequential data in other domains. We report different experiments, conducted on some manually and automatically segmented data, with different label granularities and different features to describe segments. We demonstrate that this approach is more robust than other classification methods, in particular when it uses the neighbouring context of a segment to find its type. Moreover, we highlight that the segmentation and the choice of features to describe segments are two crucial points in the labeling process.