GEOMETRIC INVARIANTS CONSTRUCTION FOR SEMANTIC SCENE UNDERSTANDING FROM MULTIPLE VIEWS INSPIRED BY THE HUMAN VISUAL SYSTEM

Semantic scene understanding is one of the several significant goals of robotics. In this paper, we propose a framework that is able to construct geometric invariants for simultaneous object detection and segmentation using a simple pairwise interactive context term, for the sake of achieving a preliminary milestone of Semantic scene understanding. The context is incorporated as pairwise interactions between pixels, imposing a prior on the labeling. Our model formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution using belief propagation and neural network. We experimentally evaluate the proposed method on three publicly available datasets: the MSRC-1, the CorelB datasets, and the PASCAL VOC database. Results show the applicability and efficacy of the proposed method to the multi-class segmentation problem.