Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2013

Abstract

In many real-word scenarios, e.g., multimedia applications, data often originates from multiple heterogeneous sources or are represented by diverse types of representation, which is often referred to as "multi-modal data". The definition of distance between any two objects/items on multi-modal data is a key challenge encountered by many real-world applications, including multimedia retrieval. In this paper, we present a novel online learning framework for learning distance functions on multi-modal data through the combination of multiple kernels. In order to attack large-scale multimedia applications, we propose Online Multi-modal Distance Learning (OMDL) algorithms, which are significantly more efficient and scalable than the state-of-the-art techniques. We conducted an extensive set of experiments on multi-modal image retrieval applications, in which encouraging results validate the efficacy of the proposed technique

Keywords

Graph Laplacian, multi-modal distance, multimedia retrieval, online learning

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

WSDM '13: Proceedings of the 6th ACM International Conference on Web Search and Data Mining: February 4-8, 2013, Rome, Italy

First Page

455

Last Page

464

ISBN

9781450318693

Identifier

10.1145/2433396.2433453

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/2433396.2433453

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