To improve the process of landmark labeling of training images, this paper proposes a new approach for estimating a set of landmarks for a large image ensemble with only a small number of manually labeled images from the ensemble.
Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, and alignment. Image labeling is typically conducted manually, which is both labor-intensive and error prone. The authors’ approach, named semi-supervised least-squares congealing, aims to minimize an objective function defined on both labeled and unlabeled images. A shape model is learned on-line to constrain the landmark configuration. The process also employs a partitioning strategy to allow coarse-to-fine landmark estimation. Extensive experiments on facial images show that the proposed approach can reliably and accurately label landmarks for a large image ensemble starting from a small number of manually labeled images, under various challenging scenarios. (Publisher abstract provided)
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