Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
M Pawan KumarDaphne Koller

Abstract

We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing an accurate linear programming based approach for selecting the best set of regions from a large dictionary. The dictionary is constructed by merging and intersecting segments obtained from multiple bottom-up over-segmentations. The linear program is solved efficiently using dual decomposition. In the context of parameter estimation, the main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for parameter estimation using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for ob...Continue Reading

References

Apr 19, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Richard SzeliskiCarsten Rother
Mar 21, 2009·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ashutosh SaxenaAndrew Y Ng

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