Convex Optimization for Scene Understanding

Mohamed Souiai, Claudia Nieuwenhuis, Evgeny Strekalovskiy, Daniel Cremers; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 9-14


In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or superpixels. The central idea is to integrate a hierarchical label prior and a set of convex constraints into the segmentation approach, which combine the three tasks by introducing high-level scene information. Instead of learning label co-occurrences from limited benchmark training data, the hierarchical prior comes naturally with the way humans see their surroundings.

Related Material

author = {Mohamed Souiai and Claudia Nieuwenhuis and Evgeny Strekalovskiy and Daniel Cremers},
title = {Convex Optimization for Scene Understanding},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}