Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2011

Abstract

With the explosive growth of social media applications on the internet, billions of social images have been made available in many social media web sites nowadays. This has presented an open challenge of web-scale social image search. Unlike existing commercial web search engines that often adopt text based retrieval, in this demo, we present a novel web-based multimodal paradigm for large-scale social image retrieval, termed "Social Image Retrieval Engine" (SIRE), which effectively exploits both textual and visual contents to narrow down the semantic gap between high-level concepts and low-level visual features. A relevance feedback mechanism is also equipped to learn with user's feedback to refine the search results interactively. Our live demo is available at http://msm.cais.ntu.edu.sg/SIRE, and a video is available athttp://www.youtube.com/user/msmntu.

Keywords

Content-based image retrieval, Multi-modal search, Social images, Social media, Text-based image retrieval

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

MM '11: Proceedings of the 19th ACM International Conference on Multimedia: November 28 - December 1, Scottsdale, AZ

First Page

817

Last Page

818

ISBN

9781450306164

Identifier

10.1145/2072298.2072474

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2072298.2072474

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