This work investigates the use of Random Forests for class based pixel-wise
segmentation of images.
The contribution of this paper is three-fold. First, we show that
apparently quite dissimilar classifiers (such as nearest neighbour
matching to texton class histograms) can be mapped onto a Random
Forest architecture. Second, based on this insight, we show that
the performance of such classifiers can be improved by incorporating
the spatial context and discriminative learning that arises naturally
in the Random Forest framework. Finally, we show that the ability
of Random Forests to combine multiple features leads to a further
increase in performance when textons, colour, filterbanks, and HOG
features are used simultaneously.
The benefit of the multi-feature classifier is demonstrated with extensive
experimentation on existing labelled image datasets. The method equals
or exceeds the state of the art on these datasets.
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