Segmentation of Natural Images by Texture and Boundary Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On the Binding Problem in Artificial Neural Networks (2020, methodology): “Alternatively, one can attempt to steer the clustering process based on the unsupervised principle of compressibility (minimum description length; Mobahi et al., 2011).”
Crisp Boundary Detection Using Pointwise Mutual Information (2014, background): “Other information-theoretic takes on segmentation have been previously explored, e.g., [28].”
Recent progress in semantic image segmentation (2018, methodology): “It derives from compressionbased method which is implemented in Mobahi et al. (2010).”
Segmentation using superpixels: A bipartite graph partitioning approach (2012, methodology): “TBES favors homogeneous regions and simple boundaries due to the minimization of “coding length”.”
Learning full pairwise affinities for spectral segmentation (2010, methodology): “The main research directions for this include mode-seeking [1], [2], deterministic annealing [3], stochastic clustering [4], [5], mixture model [6] [7], rate distortion [8], graph-based model [9], [10], [11], contourbased model [12], [13], and other variational methods [14], [15].”