Unsupervised automatic attribute discovery method via multi-graph clustering

Recently, various automated attribute discovery methods have been developed to discover useful visual attributes from a given set of images. Despite the progress made, most methods consider the supervised scenario which assumes the existence of labelled data. Recent results suggest that it is possible to discover attributes from a set of unlabelled data. In this work, we propose a novel unsupervised attribute discovery method utilising multi-graph approach that preserves both local neighbourhood structure as well as class separability. Whilst, the local neighbourhood structure is preserved by considering multiple similarity graphs, the class separability is achieved by incorporating the traditional clustering objective. For evaluation, we first investigate the performance of the proposed approach to address a clustering task. Then we apply our proposed method to automatically discover visual attributes and compare with various automatic attribute discovery and hashing methods. The results show that our proposed method is able to improve the performance in the clustering task. Furthermore, when evaluated using the recent meaningfulness metric, the proposed method outperforms the other unsupervised attribute discovery methods.