An improved random forest classifier for image classification
Baoxun Xu, Yunming Ye, Lei Nie · 2012
This paper proposes an improved random forest algorithm for image classification. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is image data. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to classify image data with a large number of object categories. With the new feature weighting method for subspace sampling and tree selection method, we can effectively reduce subspace size and improve classification performance without increasing error bound. Experimental results on image datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman's method.