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

TY - GEN

T1 - HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

AU - Manivannan, Siyamalan

AU - Li, Wenqi

AU - Akbar, Shazia

AU - Wang, Ruixuan

AU - Zhang, Jianguo

AU - McKenna, Stephen J.

PY - 2014

Y1 - 2014

N2 - 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%.

AB - 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%.

KW - HEp-2 Cell Classification

KW - multi-resolution local patterns

KW - pattern recognition

UR - https://www.scopus.com/pages/publications/84919457731

U2 - 10.1109/I3A.2014.18

DO - 10.1109/I3A.2014.18

M3 - Conference contribution

AN - SCOPUS:84919457731

SN - 9781479942527

SP - 37

EP - 40

BT - Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014

PB - IEEE Computer Society

T2 - 13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images

Y2 - 24 August 2014 through 24 August 2014

ER -