Semantic Segmentation and Object Recognition Using Scene-Context Scale

摘要

Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective region size of local context to classify an image pixel in a scene. This paper presents semantic segmentation and object recognition using scene-context scale. The scene-context scale can be estimated by the entropy of the leaf node in multi-scale text on forests. The multi-scale text on forests efficiently provide both hierarchical clustering into semantic textons and local classification depending on different scale levels. For semantic segmentation, we combine the classified category distributions of scene-context scale with the bag-of-textons model. In our experiments, we use MSRC21 segmentation dataset to assess our segmentation algorithm and show that the usage of the scene-context scale improves recognition performance.