COUPLED FEATURE MAPPING AND CORRELATION MINING FOR CROSS-MEDIA RETRIEVAL

被引：0

作者：

Fan, Mengdi ^{[1
]}

Wang, Wenmin ^{[1
]}

Wang, Ronggang ^{[1
]}

机构：

[1] Peking Univ, Shenzhen Grad Sch, Sch Elect & Comp Engn, Lishui Rd 2199, Shenzhen 518055, Peoples R China

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW) | 2016年

关键词：

Cross-media retrieval; feature mapping; homo-correlation; hetero-correlation;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Cross-media retrieval aims to integrate and analyze the features of various modalities (e.g., text, image and video) to mine their potential semantic information. In this paper, we propose a novel cross-media retrieval framework, which performs coupled feature mapping and correlation mining successively. Our method first learns two projection matrices to map the multimodal features into a common category space, in which homo-and hetero-correlation techniques can be applied easily. Homo-correlation focuses on the semantic category information within the same media type, while hetero-correlation focuses on the semantic category information between different media types. The two could complement and reinforce each other. Experiments on two different datasets, Wikipedia dataset and Pascal Voc dataset, demonstrate that the proposed framework gives promising results compared to the related state-of-the-art approaches.

引用

页数：6