Abstract. Automatic image annotation aims at predicting a set of textual labels for an image that describe its semantics. These are usually taken from an annotation vocabulary of few hundred labels. Because of the large vocabulary, there is a high variance in the number of images corresponding to different labels ("class-imbalance"). Additionally, due to the limitations of manual annotation, a significant number of available images are not annotated with all the relevant labels ("weaklabelling"). These two issues badly affect the performance of most of the existing image annotation models. In this work, we propose 2PKNN, a two-step variant of the classical K-nearest neighbour algorithm, that addresses these two issues in the image annotation task. The first step of 2PKNN uses "image-to-label" similarities, while the second step uses "image-to-image" similarities; thus combining the benefits of both. Since the performance of nearest-neighbour based methods greatly depends on how features are compared, we also propose a metric learning framework over 2PKNN that learns weights for multiple features as well as distances together. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label prediction. For scalability, we implement it by alternating between stochastic sub-gradient descent and projection steps. Extensive experiments demonstrate that, though conceptually simple, 2PKNN alone performs comparable to the current state-of-the-art on three challenging image annotation datasets, and shows significant improvements after metric learning.
REF propose a two-step variant of K-nearest neighbor approach, where the first step is to learn image-to-label similarities and the second is to learn image-to-image similarities.
Image annotation using metric learning in semantic neighbourhoods
In ECCV
Computer Science