Attribute prediction with long-range interactions via path coding

Due to the describable or human-nameable nature of visual attributes, the appropriate utilization of attributes has been receiving much attention in recent years in many applications. Motivated by the assumption that the long-range interactions between attributes can boost image understanding and classification, path coding is utilized in this paper to model the long-range interactions between attributes for the attribute prediction, we call it attribute prediction via a path coding penalty (abbreviated as AP²CP). AP²CP not only introduces structured sparsity penalties over paths on a directed acyclic graph, but also captures the intrinsical long-range dependent interactions between attributes. The proposed AP²CP can be efficiently solved by leveraging network flow optimization. The experiments show that the proposed AP²CP achieves a better performance in attribute prediction.