Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic video annotation by semi-supervised learning with kernel density estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Efficient similarity derived from kernel-based transition probability
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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The insufficiency of labeled training samples for representing the distribution of the entire data set (include labeled and unlabeled) is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, we present a novel semi-supervised approach named Kernel based Local Neighborhood Propagation (Kernel LNP) for video annotation. This approach combines the consistency assumption and the Local Linear Embedding (LLE) method in a nonlinear kernel-mapped space, which improves a recently proposed method Local Neighborhood Propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach can obtain a more accurate result than LNP for video semantic annotation.