Uniform Detection in Social Image Streams
Social
media mining from Internet has been an emerging research topic. The problem is
challenging because of massive data contents from various sources, especially
image data from user upload.
In
recent years, dictionary learning based image classification has been widely
studied and gained significant success.
In
this paper, we propose a framework for automatic detection of interested
uniforms in image streams from social networks. The systems is composed of a
powerful feature extraction module based on dense SIFT feature and a
state-of-the-art discriminative dictionary learning approach.
Beside that, a parallel implementation of
feature extraction is deployed to make the system work real time. An extensive
set of experiments has been conducted on four real-life datasets.
The
experimental results show that we can obtain the detection rate up to 100% on
some datasets. We also get real time performance with a speed of image stream
of about 40 images per second.
The
framework can be applied to emerging applications such as uniform detection,
automated image tagging, content base image retrieval or online advertisement
based on image content.
Title:
| Uniform Detection in Social Image Streams | |
| Authors: | Manh, N.Q. Tuan, N.D. Sang, D.V. Binh, H.T.T. Thuy, N.T. |
| Keywords: | Dictionary Learning Image Classification Max Pooling Social Image Mining |
| Issue Date: | 2016 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Scopus |
| Abstract: | Social media mining from Internet has been an emerging research topic. The problem is challenging because of massive data contents from various sources, especially image data from user upload. In recent years, dictionary learning based image classification has been widely studied and gained significant success. In this paper, we propose a framework for automatic detection of interested uniforms in image streams from social networks. The systems is composed of a powerful feature extraction module based on dense SIFT feature and a state-of-the-art discriminative dictionary learning approach. Beside that, a parallel implementation of feature extraction is deployed to make the system work real time. An extensive set of experiments has been conducted on four real-life datasets. The experimental results show that we can obtain the detection rate up to 100% on some datasets. We also get real time performance with a speed of image stream of about 40 images per second. The framework can be applied to emerging applications such as uniform detection, automated image tagging, content base image retrieval or online advertisement based on image content. |
| Description: | Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015 4 January 2016, Article number 7371779, Pages 180-185 |
| URI: | http://repository.vnu.edu.vn/handle/VNU_123/34102 |
| ISBN: | 978-146738013-3 |
| Appears in Collections: | Bài báo của ĐHQGHN trong Scopus |
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