Fig. 3

Illustration of multi-class-grained scanning. a Suppose four classes (A, B, C and D) in training dataset. For each class, we produce the positive and negative sub-datasets, and then use the sub-datasets to train a binary random forest classifier. Four different types of random forests will be produced by using different training datasets (sliding window based). The out-of-bagging (OOB) score of each forest is used to calculate a normalized quantity weight to each forest. b Based on the fit forests and their quantity weights, a 500-dim instance vector can be transformed to a concatenated 1604-dim representation