Drug related webpages classification using images and text information based on multi-kernel learning

Ruiguang Hu; Liping Xiao; Wenjuan Zheng

doi:10.1117/12.2205145

14 December 2015 Drug related webpages classification using images and text information based on multi-kernel learning

Ruiguang Hu, Liping Xiao, Wenjuan Zheng

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130F (2015) https://doi.org/10.1117/12.2205145
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Citation Download Citation

Ruiguang Hu, Liping Xiao, and Wenjuan Zheng "Drug related webpages classification using images and text information based on multi-kernel learning", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130F (14 December 2015); https://doi.org/10.1117/12.2205145

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