Image Segmentation  

Subspace Labeling and Fusion via Energy Minimization


People - Dr. Gregory Hager, Jason Corso, Maneesh Dewan

Description We have proposed a subspace labeling technique for global Image segmentation in a particular feature subspace is a fairly well understood problem. However, it is well known that operating in only a single feature subspace, e.g. color, texture, etc, seldom yields a good segmentation for real images. However, combining information from multiple subspaces in an optimal manner is a difficult problem to solve algorithmically. We propose a solution that fuses contributions from multiple feature subspaces using an energy minimization approach. For each subspace, we compute a per-pixel quality measure and perform a partitioning through the standard normalized cut algorithm. To fuse the subspaces into a final segmentation, we compute a subspace label for every pixel. The labeling is computed through the graph-cut energy minimization framework proposed by Boykov et al. Finally, we combine the initial subspace segmentation with the subspace labels obtained from the energy minimization to yield the final segmentation. Examples of the algorithms being studied follow:




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