Abstract

Semantic scene classification is a challenging problem in com- puter vision. In this paper, we present a novel multi-level active learn- ing approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent ob ject- based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent ob ject class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level la- bel query, which maintains the default label query at the target scene class level, but switches to the latent ob ject class level whenever an “unexpected” target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.