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HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

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Original languageEnglish
Title of host publicationProceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014
PublisherIEEE Computer Society
Pages37-40
Number of pages4
ISBN (Print)9781479942527
DOIs
StatePublished - 2014
Event13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm, Sweden

Workshop

Workshop13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images
CountrySweden
CityStockholm
Period24/08/1424/08/14
Internet address

Abstract

We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.

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