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%.
|Title of host publication||Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published - 2014|
|Event||13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm Waterfront, Stockholm, Sweden|
Duration: 24 Aug 2014 → 24 Aug 2014
|Workshop||13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images|
|Period||24/08/14 → 24/08/14|
- HEp-2 Cell Classification
- multi-resolution local patterns
- pattern recognition
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Student thesis: Doctoral Thesis › Doctor of PhilosophyFile