Mining visualness
To understand which concepts are visualizable and to what extent they can be visualized are worthwhile for multimedia and computer vision research. Unfortunately, few previous works have ever touched such topics. In this paper, we propose an unified model to automatically identify visual concepts and estimate their visual characteristics, or visualness, from a large-scale image dataset. To this end, an image heterogeneous graph is first built to integrate various visual features, and then a simultaneous ranking and clustering algorithm is introduced to generate visually and semantically compact image clusters, named visualsets. Based on the visualsets, visualizable concepts are discovered and their visualness scores are estimated. The experimental results demonstrate the effectiveness of the proposed schema.