  Understanding a visual scene incorporates objects, relationships, and
context. Traditional methods working on an image mostly focus on object
detection and fail to capture the relationship between the objects.
Relationships can give rich semantic information about the objects in a scene.
The context can be conducive to comprehending an image since it will help us to
perceive the relation between the objects and thus, give us a deeper insight
into the image. Through this idea, our project delivers a model that focuses on
finding the context present in an image by representing the image as a graph,
where the nodes will the objects and edges will be the relation between them.
The context is found using the visual and semantic cues which are further
concatenated and given to the Support Vector Machines (SVM) to detect the
relation between two objects. This presents us with the context of the image
which can be further used in applications such as similar image retrieval,
image captioning, or story generation.
