Multi-Attributed Graph Matching: a Spectral Clustering Approach
نویسندگان
چکیده
We present a Multiple Attribute Graph (MARG) Matching algorithm. The algorithm operates on a strong product graph, where vertices correspond to hypothesized matchings between two nodes and edges specify compatibilities between two matches. We show how the matching problem reduces to partitioning the strong product graph, which we solve with spectral clustering. We test our algorithm on a few images and handwritten digits, building a MARG for each image with spatial edge attributes.
منابع مشابه
Clustering Attributed Multi-graphs with Information Ranking
Attributed multi-graphs are data structures to model realworld networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multigraphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Infor...
متن کاملB-Matching for Spectral Clustering
We propose preprocessing spectral clustering with b-matching to remove spurious edges in the adjacency graph prior to clustering. B-matching is a generalization of traditional maximum weight matching and is solvable in polynomial time. Instead of a permutation matrix, it produces a binary matrix with rows and columns summing to a positive integer b. The b-matching procedure prunes graph edges s...
متن کاملSpectral clustering for divide-and-conquer graph matching
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic block model random graphs are correctly matched utilizing very few seeds through...
متن کاملCentralized Clustering Method To Increase Accuracy In Ontology Matching Systems
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory con...
متن کاملAFRL-RY-WP-TP-2007-1223, V2 DIFFUSION MAPS AND GEOMETRIC HARMONICS FOR AUTOMATIC TARGET RECOGNITION (ATR) Volume 2: Appendices
Data fusion and multi-cue data matching are fundamental tasks of high-dimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is three-fold. First, we present the Laplace-Beltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005