Abstract
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.
Original language | English |
---|---|
Title of host publication | Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017) |
Editors | Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler , Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J. F. Jones, Martha Larson |
Publisher | CEUR-WS |
Number of pages | 3 |
Publication status | Published - Sept 2017 |
Event | 2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland Duration: 13 Sept 2017 → 15 Sept 2017 https://ceur-ws.org/Vol-1984/ |
Publication series
Name | CEUR Workshop Proceedings |
---|---|
Publisher | CEUR-WS |
Volume | 1984 |
ISSN (Print) | 1613-0073 |
Conference
Conference | 2017 Multimedia Benchmark Workshop, MediaEval 2017 |
---|---|
Country/Territory | Ireland |
City | Dublin |
Period | 13/09/17 → 15/09/17 |
Internet address |
Scopus Subject Areas
- General Computer Science