A Graph-based Context Learning Technique for Image Parsing

The modern deep learning-based architectures have performed well for pixel-wise segmentation tasks. The consideration of context is of vital importance for generation of accurate semantic information. In this research, a deep learning-based image parsing framework is proposed that utilizes novel relation-aware context learning technique. The proposed technique explores the graph constructs from the training data to learn the co-occurring context associations of object category labels using the graph edge connections. The proposed graph-based context learning technique defines the scene specific relation-awareness among semantic object categories, e.g., the probability of sky, road and building to co-exist in a scene is high. The proposed image parsing architecture (including the novel graph-based context learning technique) is evaluated on the benchmark datasets. In addition, a comprehensive comparison with existing image parsing techniques is presented to establish the efficacy of the scene-graph generation. The in-depth investigation of graph generation is presented to demonstrate the improvement in pixel-wise labeling.