Transductive cartoon retrieval by multiple hypergraph learning

Conference Name:19th International Conference on Neural Information Processing, ICONIP 2012. Conference Address: Doha, Qatar. Time:November 12, 2012 - November 15, 2012.United Development Company PSC (UDC); Qatar Petrochemical Company; ExxonMobil; Qatar Petroleum; Texas A and M University at Qatar; Asia Pacific Neural Network AssemblyCartoon characters retrieval frequently suffers from the distance estimation problem. In this paper, a multiple hypergraph fusion based approach is presented to solve this problem. We build multiple hypergraphs on cartoon characters based on their features. In these hypergraphs, each vertex is a character, and an edge links to multiple vertices. In this way, the distance estimation between characters is avoided and the high-order relationship among characters can be explored. The experiments of retrieval are conducted on cartoon datasets, and the results demonstrate that the proposed approach can achieve better performance than state-of-the-arts methods. 漏 2012 Springer-Verlag

Transductive cartoon retrieval by multiple hypergraph learning

Cartoon characters retrieval frequently suffers from the distance estimation problem. In this paper, a multiple hypergraph fusion based approach is presented to solve this problem. We build multiple hypergraphs on cartoon characters based on their features. In these hypergraphs, each vertex is a character, and an edge links to multiple vertices. In this way, the distance estimation between characters is avoided and the high-order relationship among characters can be explored. The experiments of retrieval are conducted on cartoon datasets, and the results demonstrate that the proposed approach can achieve better performance than state-of-the-arts methods. © 2012 Springer-Verlag