A real-world object surface often consists of multiple materials. Recognizing surface materials is important because it significantly benefits understanding the quality and functionality of the object. However, identifying multiple materials on a surface from a single photograph is very challenging because different materials are often interweaved together and hard to be segmented for separate identification. To address this problem, we present a multi-label learning framework for identifying multiple materials of a real-world object surface without a segmentation for each of them. We find that there are potential correlations between materials and that correlations are relevant to object category. For example, a surface of monitor likely consists of plastic and glasses rather than wood or stone. It motivates us to learn the correlations of material labels locally on each semantic object cluster. To this end, samples are semantically grouped according to their object categories. For each group of samples, we employ a Directed Acyclic Graph (DAG) to encode the conditional dependencies of material labels. These object-specific DAGs are then used for assisting the inference of surface materials. The key enabler of the proposed method is that the object recognition provides a semantic cue for material recognition by formulating an object-specific DAG learning. We test our method on the ALOT database and show consistent improvements over the state-of-the-arts. 
Learning object-specific DAGs for multi-label material recognition