Segmentation using superpixels: A bipartite graph partitioning approach

By Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

325 citations recorded by the source corpus.

How researchers see this paper

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Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs (2016, methodology): “[6] used multi-layered superpixels to capture diverse and multi-scale visual patterns of images based on bipartite graph partitioning.”
Locally Weighted Ensemble Clustering (2016, methodology): “4: Partition the LWBG into a certain number of segments using the Tcut algorithm [50].”
Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019, methodology): “By taking advantage of the bipartite graph structure, the transfer cut [6] can thereby be used to efficiently partition the graph and achieve the final clustering”
A survey of graph theoretical approaches to image segmentation (2013, methodology): “To quantitatively evaluate the segmentation results, as in [133,134]) we use five well-known indices: Probabilistic Rand (PR) index [107], Normalized Probabilistic Rand (NPR) index [107], Variation of Information (VI) [131], Global Consistency Error (GCE) [106] and Boundary Displacement Error (BDE) ”
Lazy Random Walks for Superpixel Segmentation (2014, background): “SUPERPIXELS are commonly defined as contracting and grouping uniform pixels in the image, which have been widely used in many computer vision applications such as image segmentation and object recognition [10], [31].”