Interpreting Context of Images using Scene Graphs

TL;DR

This paper proposes a scene graph-based model to interpret image context by representing objects and their relationships, aiding in understanding and applications like image retrieval and captioning.

Contribution

It introduces a novel approach that combines visual and semantic cues in scene graphs and uses SVMs to detect object relations, enhancing image understanding.

Findings

Effective scene graph representation of images.

Improved relation detection using combined cues.

Potential applications in image captioning and retrieval.

Abstract

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.