Non-metric affinity propagation for unsupervised image categorization

By Delbert Dueck, Brendan J. Frey. IEEE International Conference on Computer Vision, 2007

368 citations recorded by the source corpus.

How researchers see this paper

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

Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, methodology): “We follow previous work (Dueck and Frey 2007) and select the widely used 7 classes, i.”
Multi-view Subspace Clustering (2015, background): “• Caltech101-7 [7] is a widely used subset of the image dataset Caltech101 which contains 101 categories.”
Shape discovery from unlabeled image collections (2009, result): “Our results on benchmark datasets demonstrate the approach can successfully discover shapes from unlabeled images.”
Foreground Focus: Unsupervised Learning from Partially Matching Images (2009, methodology): “We first produce results using our algorithm with two base feature types for our semi-local descriptor: 1) densely sampled SIFT descriptors, and 2) SIFT descriptors detected using Lowe’s Difference of Gaussians (DoG) scale space selection (the same setting as in Dueck and Frey 2007).”
Non-maximum Suppression for Object Detection by Passing Messages Between Windows (2014, background): “APC has been applied to a variety of problems [33–36] and extended in multiple ways.”
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity (2018, background): “This appears in facility location problems where opening centers is expensive and in exemplar-based clustering (Dueck & Frey, 2007).”
Multi-View K-Means Clustering on Big Data (2013, neutral): “Following [Dueck and Frey, 2007], we sample the data and totally we have 441 images.”