Biological Image Classification with Random Subwindows and Extra-Trees

We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based on random subwindows extraction and the combination of their classification using ensembles of extremely randomized decision trees. 1 Method

[1]  C. Conrad,et al.  Automatic identification of subcellular phenotypes on human cell arrays. , 2004, Genome research.

[2]  Daniel Martin Keysers,et al.  Modeling of image variability for recognition , 2006, Ausgezeichnete Informatikdissertationen.

[3]  Raphaël Marée,et al.  Random subwindows for robust image classification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[5]  Clement H. C. Leung,et al.  Linguistic Estimation of Topic Difficulty in Cross-Language Image Retrieval , 2005, CLEF.

[6]  Thomas Martin Deserno,et al.  The CLEF 2005 Cross-Language Image Retrieval Track , 2005, CLEF.