Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Gradual transition detection with conditional random fields
Proceedings of the 15th international conference on Multimedia
An Inductive Logic Programming-Based Approach for TV Stream Segment Classification
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Fast structuring of large television streams using program guides
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
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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.