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An online-optimized incremental learning framework for video semantic classification

9 years 5 months ago
An online-optimized incremental learning framework for video semantic classification
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic classification framework, termed OOIL (for Online-Optimized Incremental Learning), in which two sets of optimized classification models, local and global, are online trained by sufficiently exploiting both local and global statistic characteristics of videos. The global models are pre-trained on a relatively small set of pre-labeled samples. And the local models are optimized for the under-test video or video segment by checking a small portion of unlabeled samples in this video, while they are also applied to incrementally update the global models. Experiments have illustrated promising results on simulated data as well as real sports videos. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing-indexing methods; I.2.10 [Artificial Intelligence]: Vision an...
Jun Wu, Xian-Sheng Hua, HongJiang Zhang, Bo Zhang
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where MM
Authors Jun Wu, Xian-Sheng Hua, HongJiang Zhang, Bo Zhang
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