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Utilizing Context Information to Enhance Content-Based Image Classification

Utilizing Context Information to Enhance Content-Based Image Classification

Qiusha Zhu, Lin Lin, Mei-Ling Shyu, Dianting Liu
Copyright: © 2011 |Volume: 2 |Issue: 3 |Pages: 18
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781613508534|DOI: 10.4018/jmdem.2011070103
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MLA

Zhu, Qiusha, et al. "Utilizing Context Information to Enhance Content-Based Image Classification." IJMDEM vol.2, no.3 2011: pp.34-51. http://doi.org/10.4018/jmdem.2011070103

APA

Zhu, Q., Lin, L., Shyu, M., & Liu, D. (2011). Utilizing Context Information to Enhance Content-Based Image Classification. International Journal of Multimedia Data Engineering and Management (IJMDEM), 2(3), 34-51. http://doi.org/10.4018/jmdem.2011070103

Chicago

Zhu, Qiusha, et al. "Utilizing Context Information to Enhance Content-Based Image Classification," International Journal of Multimedia Data Engineering and Management (IJMDEM) 2, no.3: 34-51. http://doi.org/10.4018/jmdem.2011070103

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Abstract

Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.

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