Fusing Multi-type Features with Distance Metric Learning for Social Image Application
Pages 403 - 406
Abstract
One of the key problems of image retrieval and classification is how to measure the distance between any two social images. However, unlike traditional images, social images usually contain multi-types of features (e.g., visual content, textual description, users' social relation information, etc.). In this paper, we propose to integrate textual description and user's social information for distance metric learning of social image. To effectively using social knowledge found from social images, we proposed a novel distance metric learning scheme, which not only exploits both visual and textual contents of social images, but also integrates user information into the learning framework. By applying the proposed technique to the image retrieval and classification tasks in our experiments, the superiority of our proposed approach over state-of-the-art approaches is demonstrated.
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Index Terms
- Fusing Multi-type Features with Distance Metric Learning for Social Image Application
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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