Multiple Semantic Matching on Augmented N-Partite Graph for Object Co-Segmentation
Published in IEEE Transactions on Image Processing, 2017
Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object co-segmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure.
| Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object co-segmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. |  |
Download paper here Recommended citation: Chuan Wang, Hua Zhang, Liang Yang, Xiaochun Cao, Hongkai Xiong. Multiple Semantic Matching on Augmented N-Partite Graph for Object Co-Segmentation. IEEE Trans. Image Process. 26(12): 5825-5839 (2017).