Semantic segmentation based on fusion of features and classifiers

This paper proposes a feed forward architecture algorithm using fusion of features and classifiers for semantic segmentation. The algorithm consists of three phases: Firstly, the features from hierarchical convolutional neural network (CNN) and the features based on region are extracted and fused on super pixel level; secondly, multiple classifiers of Softmax, XGBoost and Random Forest are ensemble to compute the per-pixel class probabilities; at last, a fully connected conditional random field is employed to enhance the final performance. The hierarchical features contain more global evidence and the region features contain more local evidence. So the fusion of these two features is expected to enhance the feature representation ability. In classification phase, integrating multiple classifiers aims to improve the generalization ability of classification algorithms. Experiments are conducted on Sift-Flow datasets by our proposed methods with competitive labeling accuracy.