Abstract: A geometric labeling method of image regions was proposed based on combination of multiple features. First of all， according to the requirement of multi-scale feature information extraction， a novel network structure—multi-scale kernel convolutional network (MSKCN) was proposed. The multi-scale feature information was used for inferring geometric label of pixel. The geometric labeling of super-pixel regions with the image super-pixel segmentation was achieved. Then a feature representation of super-pixel regions was established by combining multi-scale features proposed and traditional features of super-pixel regions. Finally， a CRF(conditional random field) model was constructed for the super-pixel image to infer geometric label of super-pixel regions with the image super-pixel segmentation. The experiments on public database Geometric Context (GC) indicated that the accuracy of geometric labeling was improved by using the proposed method compared with the existing state-of-art.

Key words: combination of multiple features, multi-scale kernel convolutional network, geometric labeling of image regions, feature learning, conditional random field model