Probabilistic graph and hypergraph matching

By Ron Zass, Amnon Shashua. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

418 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bfa7d96549deca45ce66b2f89d92870981a45206

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Citations in the corpus

Adaptive Hypergraph Learning and its Application in Image Classification (2012, neutral): “Zass and Shashua [21] adopted the hypergraph in image matching by using convex optimization.”
An Integer Projected Fixed Point Method for Graph Matching and MAP Inference (2009, methodology): “The algorithms we chose for comparison and also for combining with ours are among the current state-of-the-art in the literature: spectral matching with affine constraints (SMAC) [3], spectral matching (SM) [5], probabilistic graph matching (PM) [2], and graduated assignment (GA) [4].”
Deformable Medical Image Registration: A Survey (2013, methodology): “Zass and Shashua in [291] proposed a similar formulation, while using a different optimization method.”
Factorized Graph Matching (2016, methodology): “The idea of matching edges has also been used in the probabilistic matching algorithm [40], where Zass and Shashua proposed to maximize the correlation between X and G 1 K q G T 2 .”
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers (2014, background): “Due to its generality and flexibility, this formulation and its extensions has been favored in recent graph matching research [6, 7, 10, 18, 20, 24, 31, 32].”
Graph-based analysis of physical exercise actions (2013, background): “Hyper-graphs are a generalization of graphs allowing for edges, so called hyper-edges, to connect any number of nodes, typically more than two [24].”
Image retrieval via probabilistic hypergraph ranking (2010, background): “[32] presents a hypergraph based image matching problem represented as a convex optimization problem.”
A comprehensive survey on point cloud registration (2021, methodology): “For example, Zass et al. [113] design a probabilistic approach to solve the high-order graph matching problem.”
Reweighted Random Walks for Graph Matching (2010, methodology): “In our experimental setup, there were three kinds of independent variables: outliers n P out and n Q out , deformation noise σ, and 3 http://www.seas.upenn.edu/ ~ timothee/ 4 http://www.cs.huji.ac.il/ ~ zass/ 5 The original code of HGM [6] uses a high-order affinity, but we edited it to compare with”
Clique-graph matching by preserving global & local structure (2015, methodology): “On the other hand, it is mathematically difficult to impose the mapping constraints on the objective function to induce the stable solution.”
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding (2014, methodology): “For instance, Shashua [24] utilized the hypergraph for image matching using convex optimization.”
3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, methodology): “A probabilistic hypergraph matching method is proposed in [43] to match two feature sets.”
Fast Exact Hyper-graph Matching with Dynamic Programming for Spatio-temporal Data (2014, neutral): “Zass and Shashua [59] presented a soft hyper-graph matching method between sets of features that proceeds through an iterative successive projection algorithm in a probabilistic setting.”
Ieee Transactions on Knowledge and Data Engineering Context-aware Hypergraph Construction for Robust Spectral Clustering (neutral): “This has been applied to many domains such as image matching [19], multi-label classification [20], video object segmentation [21], and image retrieval [22].”
Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “Representative studies on hypergraph matching include Zass and Shashua (2008), Chertok and Keller (2010), Lee et al. (2011), Chang and Kimia (2011), Duchenne et al. (2011) and Yan et al. (2015d).”