Latent Fisher Discriminant Analysis
This work addresses semi-supervised classification problems in domains like object detection and keyframe extraction where consistent instance labels are unavailable, offering an incremental improvement over existing methods.
The paper tackled the limitation of Linear Discriminant Analysis (LDA) in semi-supervised classification by proposing a latent variable Fisher discriminant analysis model that relaxes instance-level labeling to bag-level, achieving competitive results on MUSK and Corel datasets and extracting more semantically meaningful keyframes on the TRECVID MED11 dataset.
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. Previous studies have also extended the binary-class case into multi-classes. However, many applications, such as object detection and keyframe extraction cannot provide consistent instance-label pairs, while LDA requires labels on instance level for training. Thus it cannot be directly applied for semi-supervised classification problem. In this paper, we overcome this limitation and propose a latent variable Fisher discriminant analysis model. We relax the instance-level labeling into bag-level, is a kind of semi-supervised (video-level labels of event type are required for semantic frame extraction) and incorporates a data-driven prior over the latent variables. Hence, our method combines the latent variable inference and dimension reduction in an unified bayesian framework. We test our method on MUSK and Corel data sets and yield competitive results compared to the baseline approach. We also demonstrate its capacity on the challenging TRECVID MED11 dataset for semantic keyframe extraction and conduct a human-factors ranking-based experimental evaluation, which clearly demonstrates our proposed method consistently extracts more semantically meaningful keyframes than challenging baselines.