Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Making use of the most expressive jumping emerging patterns for classification
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
| Hi-index | 0.89 |
The method based on Bag-of-visual-Words (BoW) deriving from local keypoints has recently appeared promising for video annotation. Visual word weighting scheme has critical impact to the performance of BoW method. In this paper, we propose a new visual word weighting scheme which is referred as emerging patterns weighting (EP-weighting). The EP-weighting scheme can efficiently capture the co-occurrence relationships of visual words and improve the effectiveness of video annotation. The proposed scheme firstly finds emerging patterns (EPs) of visual keywords in training dataset. And then an adaptive weighting assignment is performed for each visual word according to EPs. The adjusted BoW features are used to train classifiers for video annotation. A systematic performance study on TRECVID corpus containing 20 semantic concepts shows that the proposed scheme is more effective than other popular existing weighting schemes.