Utilizing co-occurrence patterns for semantic concept detection in images

Semantic concept detection is an important open problem in concept-based image understanding. In this paper, we develop a method inspired by social network analysis to solve the semantic concept detection problem. The novel idea proposed is the detection and utilization of concept co-occurrence patterns as contextual clues for improving individual concept detection. We detect the patterns as hierarchical communities by graph modularity optimization in a network with nodes and edges representing individual concepts and co-occurrence relationships. We evaluate the effect of detected co-occurrence patterns in the application scenario of automatic image annotation. Experimental results on SUN'09 and OSR datasets demonstrate our approach achieves significant improvements over popular baselines.