Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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This paper demonstrates a new approach to detecting high-level events that may be depicted in images or video frames. Given a non-annotated content item, a large number of previously trained visual concept detectors are applied to it and their responses are used for representing the content item with a model vector in a high-dimensional concept space. Subsequently, an improved subclass discriminant analysis method is used for identifying a concept subspace within the aforementioned concept space, that is most appropriate for detecting and recognizing the target high-level events. In this subspace, the nearest neighbor rule is used for comparing the non-annotated content item with a few known example instances of the target events. The high-level events used as target events in the present version of the system are those defined for the TRECVID 2010 Multimedia Event Detection (MED) task.