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Scene labeling task is to segment the image into meaningful regions andcategorize them into classes of objects which comprised the image. Commonlyused methods typically find the local features for each segment and label themusing classifiers. Afterward, labeling is smoothed in order to make sure thatneighboring regions receive similar labels. However, they ignore expressive andnon-local dependencies among regions due to expensive training and inference.In this paper, we propose to use high-level knowledge regarding rules in theinference to incorporate dependencies among regions in the image to improvescores of classification. Towards this aim, we extract these rules from dataand transform them into constraints for Integer Programming to optimize thestructured problem of assigning labels to super-pixels (consequently pixels) ofan image. In addition, we propose to use soft-constraints in some scenarios,allowing violating the constraint by imposing a penalty, to make the model moreflexible. We assessed our approach on three datasets and obtained promisingresults.

Scene Labeling Through Knowledge-Based Rules Employing Constrained Integer Linear Programing