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Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch...
In this paper, we propose a novel semi-supervised learning strategy to address the problem of celebrity identification. The video context information is explored to facilitate the learning process based on the assumption that faces in the same video track share the same identity. Once a frame within a track is recognized confidently, the label can be propagated through the whole track, referred to...
This paper presents a new incremental learning solution for linear discriminant analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to incremental LDA than previous methods. It also significantly...
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