TY - GEN
T1 - HKBU at mediaeval 2017 medico
T2 - 2017 Multimedia Benchmark Workshop, MediaEval 2017
AU - LIU, Yang
AU - Gu, Zhonglei
AU - CHEUNG, Kwok Wai
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.
PY - 2017/9
Y1 - 2017/9
N2 - In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.
AB - In this paper, we describe our model designed for automatic detection of diseases based on multimedia data collected in hospitals. Specifically, a two-stage learning strategy is designed to predict the diseases. In the first stage, a dimensionality reduction method called bidirectional marginal Fisher analysis (BMFA) is proposed to project the original data to the low-dimensional space, with the key discriminant information being well preserved. In the second stage, the multi-class support vector machine (SVM) is utilized on the low-dimensional space for detection. Experimental results demonstrate the efficiency of designed model.
UR - http://www.scopus.com/inward/record.url?scp=85035001700&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85035001700
VL - 1984
T3 - CEUR Workshop Proceedings
BT - MediaEval 2017
PB - CEUR-WS
Y2 - 13 September 2017 through 15 September 2017
ER -