Large Margin Image Set Representation and Classification
This addresses the problem of classifying sets of images, such as in video-based face recognition, with incremental improvements over existing methods.
The paper tackles image set classification by maximizing the margin between sets of different classes, using a model that combines image samples and affine hulls, optimized with an EM and APG algorithm. Experiments on video-based face recognition show it significantly outperforms state-of-the-art methods in effectiveness and efficiency.
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.