Deep network for visual saliency prediction by encoding image composition

This article will be visual significance into the graphical guidance (the chart is a medium-sized join subgraph). Deep structure, from the level of learning a significant map. The original image pixel to the object level graphic (oGL), and further Space level graphics (sGL). In particular, we first sample Super pixels from each image, and they are used as buildings Block of each object. In order to seamlessly describe different objects, the number of oGLs is generated by spatial adjacent links. The super pixel oGL object response mapping is obtained by obtaining, Transfer, the semantics of the image tag to oGL. As space, the layout of the object plays an important role in the prominence of the object based on the relevant learning distribution proposed sGL OGL position between. Finally, in order to imitate the “winner of all” Biological vision mechanism, the largest majority of voting programs, The sGL of the image, is probabilistically combined into a significant graph. Experimental results show that oGLs and sGLs capture the object level well and space-level visual cues, resulting in competitiveness significant detection accuracy.