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Sometimes the meaning conveyed by images goes beyond the list of objects theycontain; instead, images may express a powerful message to affect the viewers'minds. Inferring this message requires reasoning about the relationshipsbetween the objects, and general common-sense knowledge about the components.In this paper, we use a scene graph, a graph representation of an image, tocapture visual components. In addition, we generate a knowledge graph usingfacts extracted from ConceptNet to reason about objects and attributes. Todetect the symbols, we propose a neural network framework named SKG-Sym. Theframework first generates the representations of the scene graph of the imageand its knowledge graph using Graph Convolution Network. The framework thenfuses the representations and uses an MLP to classify them. We extend thenetwork further to use an attention mechanism which learn the importance of thegraph representations. We evaluate our methods on a dataset of advertisements,and compare it with baseline symbolism classification methods (ResNet and VGG).Results show that our methods outperform ResNet in terms of F-score and theattention-based mechanism is competitive with VGG while it has much lower modelcomplexity.

Symbolic image detection using scene and knowledge graphs