Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A novel kernel-based maximum a posteriori classification method
Neural Networks
Color face recognition for degraded face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Mining weakly labeled web facial images for search-based face annotation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Retrieval-based face annotation by weak label regularized local coordinate coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Unsupervised face-name association via commute distance
Proceedings of the 20th ACM international conference on Multimedia
A unified learning framework for auto face annotation by mining web facial images
Proceedings of the 21st ACM international conference on Information and knowledge management
Online annotation of faces in personal videos by sequential learning
Multimedia Tools and Applications
Learning to name faces: a multimodal learning scheme for search-based face annotation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Annotation propagation in image databases using similarity graphs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of our approach is to solve the Fisher's discriminant using deformed kernels incorporating the information of both labeled and unlabeled data. To evaluate the effectiveness of our method, we have conducted extensive experiments on three types of multimedia testbeds: the FRGC benchmark face dataset, the Yahoo! web image collection, and the TRECVID video data collection. The experimental results show that our TKFD algorithm is more effective than traditional supervised approaches, especially when there are very few training data.