Abstract
We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for solving several general problems in computer vision, ranging from image classification and segmentation to content-based image retrieval and interest point detection. The methods are illustrated on various applications and datasets from the biomedical domain.
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Notes
- 1.
We restrict our discussion here to numerical features and refer the interested reader to [128] for the treatment of discrete features.
- 2.
Intuitively, as it is less likely a priori that two examples will fall together in a small leaf, it is natural to consider them very similar when they actually do.
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Acknowledgements
R.M. is supported by the CYTOMINE research grant (number 1017072) of the Wallonia (DGO6) and by the GIGA center with the help of the Wallonia and the European Regional Development Fund (ERDF). P.G. is a research associate of the FNRS, Belgium.
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Marée, R., Wehenkel, L., Geurts, P. (2013). Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_10
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