Joint Graph Regularization based on Modality-dependent Cross-media Retrieval
Joint Graph Regularization based on Modality-dependent Cross-media Retrieval
复制标题
基于联合图正则化的模态相关跨媒体检索
DOI：
10.1007/s11042-017-4918-0
复制
发表时间：
--
影响因子：
3.6
通讯作者：
Xiao Dong
中科院分区：
文献类型：
--
作者：
Jihong Yan;Huaxiang Zhang;Ji;e Sun;Qiang Wang;Peilian Guo;Lili Meng;Wenbo Wan;Xiao Dong
Cross-media retrieval returns heterogeneous multimedia data of the same semantics for a query object, and the key problem for cross-media retrieval is how to deal with the correlations of heterogeneous multimedia data. Many works focus on mapping different modal data into an isomorphic space, so the similarities between different modal data can be measured. Inspired by this idea, we propose a joint graph regularization based modality-dependent cross-media retrieval approach (JGRMDCR), which takes into account the one-to-one correspondence between different modal data pairs, the inter-modality similarities and the intra-modality similarities. Meanwhile, according to the modality of the query object, this method learns different projection matrices for different retrieval tasks. Experimental results on benchmark datasets show that the proposed approach outperforms the other state-of-the-art algorithms.
