HKBU at mediaeval 2017 medico: Medical multimedia task

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

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 languageEnglish
Title of host publicationMediaEval 2017
PublisherCEUR-WS
Volume1984
Publication statusPublished - Sep 2017
Event2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland
Duration: 13 Sep 201715 Sep 2017

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
ISSN (Print)1613-0073

Conference

Conference2017 Multimedia Benchmark Workshop, MediaEval 2017
Country/TerritoryIreland
CityDublin
Period13/09/1715/09/17

Scopus Subject Areas

  • Computer Science(all)

Fingerprint

Dive into the research topics of 'HKBU at mediaeval 2017 medico: Medical multimedia task'. Together they form a unique fingerprint.

Cite this