Scene segmentation has always been a key and complicated problem in machine learning. In order to understand the scene and recognize the objects more accurately

this paper adopts human attention mechanism

takes the category semantic information into consideration and merges it into the image feature learning. The Focus+Context semantic representation is proposed

where the context describes the relationship between the focus and different objects in the scene

and the focus shared among the same category are composed of similar clusters. The probabilistic topic model is used to compute the local features as well as their semantic information. The experimental results show that the Focus+Context method increases the recognition rate of the scene objects

and specially

the proposed method

in a local and global understanding way

can simplify the scene recognition greatly under a small sample size.