Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Estimating average precision with incomplete and imperfect judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Correlative multilabel video annotation with temporal kernels
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multi-cue fusion for semantic video indexing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video
IEEE Transactions on Multimedia
Mining concept relationship in temporal context for effective video annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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This paper proposes a new approach to refine video annotation by exploiting the inter-shot context. Our method is mainly novel in two ways. On one hand, to refine annotation result of the target concept, we model the sequence of shots in video as a conditional random field with chain structure. In this way, we can capture different kinds of concept relationships in inter-shot context to improve the annotation accuracy. On the other hand, to exploit inter-shot context for the target concept, we classify shots into different types according to their correlation to the target concept, which will be used to represent different kinds of concept relationships in inter-shot context. Experiments on the widely used TRECVID 2006 data set show that our method is effective for refining video annotation, achieving a significant performance improvement over several state of the art methods.