Identifying Reliable Annotations for Large Scale Image Segmentation
This addresses the costly and tedious annotation process for semantic segmentation, offering a solution for researchers and practitioners dealing with noisy data sources like Amazon Mechanical Turk.
The paper tackles the problem of unreliable annotations in large-scale image segmentation by introducing a Gaussian process-based method that identifies unreliable annotations and suppresses their negative effects, achieving scalability to datasets with millions of training instances.
Challenging computer vision tasks, in particular semantic image segmentation, require large training sets of annotated images. While obtaining the actual images is often unproblematic, creating the necessary annotation is a tedious and costly process. Therefore, one often has to work with unreliable annotation sources, such as Amazon Mechanical Turk or (semi-)automatic algorithmic techniques. In this work, we present a Gaussian process (GP) based technique for simultaneously identifying which images of a training set have unreliable annotation and learning a segmentation model in which the negative effect of these images is suppressed. Alternatively, the model can also just be used to identify the most reliably annotated images from the training set, which can then be used for training any other segmentation method. By relying on "deep features" in combination with a linear covariance function, our GP can be learned and its hyperparameter determined efficiently using only matrix operations and gradient-based optimization. This makes our method scalable even to large datasets with several million training instances.