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Proceedings Paper

Drug-related webpages classification based on multi-modal local decision fusion
Author(s): Ruiguang Hu; Xiaojing Su; Yanxin Liu
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Paper Abstract

In this paper, multi-modal local decision fusion is used for drug-related webpages classification. First, meaningful text are extracted through HTML parsing, and effective images are chosen by the FOCARSS algorithm. Second, six SVM classifiers are trained for six kinds of drug-taking instruments, which are represented by PHOG. One SVM classifier is trained for the cannabis, which is represented by the mid-feature of BOW model. For each instance in a webpage, seven SVMs give seven labels for its image, and other seven labels are given by searching the names of drug-taking instruments and cannabis in its related text. Concatenating seven labels of image and seven labels of text, the representation of those instances in webpages are generated. Last, Multi-Instance Learning is used to classify those drugrelated webpages. Experimental results demonstrate that the classification accuracy of multi-instance learning with multi-modal local decision fusion is much higher than those of single-modal classification.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090S (8 March 2018); doi: 10.1117/12.2284909
Show Author Affiliations
Ruiguang Hu, Beijing Aerospace Automatic Control Institute (China)
Xiaojing Su, Beijing Aerospace Automatic Control Institute (China)
Yanxin Liu, Beijing Aerospace Automatic Control Institute (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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