Publication Type

Conference Proceeding Article

Publication Date

2-2011

Abstract

With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications.

Keywords

distance metric learning, inductive learning, social images, automated image tagging, transductive learning

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

WSDM'11: proceedings of the 4th International Conference on Web Search and Data Mining: Hong Kong, China, February 9-12, 2011

First Page

197

Last Page

206

ISBN

9781450304931

Identifier

10.1145/1935826.1935865

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/1935826.1935865

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