Paper Info
Title
Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval.
Abstract
As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach. © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Year
DOI
Venue
2013
null
AAAI
Keywords
Field
DocType
null
Graph,Data mining,Semantic relationship,External Data Representation,Label propagation,Computer science,Cross media,Graph regularization,Artificial intelligence,Big data,Machine learning,The Internet
Conference
Volume
Issue
Citations 
null
null
47
PageRank 
References 
Authors
1.10
14
3
Name
Order
Citations
PageRank
Xiaohua Zhai120913.00
Yuxin Peng2112274.90
Jianguo Xiao377149.67