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Modern deep learning algorithms have triggered various image segmentation approaches. However, most of them deal with pixel-based segmentation. Superpixels, on the other hand, provide a certain degree of contextual information while reducing computation cost. In our approach, we have performed superpixel-level semantic segmentation considering three various levels as neighbors for semantic contexts. Furthermore, we have enlisted a number of ensemble approaches like max-voting and weighted average. We have also used the Dempster–Shafer theory of uncertainty to analyze confusion among various classes. Our method has proved to be superior to a number of different modern approaches on the same dataset.

Combining Multilevel Contexts of Superpixel Using Convolutional Neural Networks to Perform Natural Scene Labeling