Titlebook: ;

covert

Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectorsnvectors of the graph Laplacian. Given two sets of eigenvectors that correspond to the smallest non-null eigenvalues of the Laplacian matrices of two graphs, we project each graph onto its eigenenvectors. We estimate the histograms of these one-dimensional graph projections (eigenvector histograms)

极小

Graph Matching Based on Node Signaturespological node signatures. Using these signatures, we compute an optimum solution for node-to-node assignment with the Hungarian method and propose a distance formula to compute the distance between weighted graphs. The experiments demonstrate that the newly presented algorithm is well suited to pat

一条卷发

受伤

jeopardize

Pairwise Similarity Propagation Based Graph Clustering for Scalable Object Indexing and Retrievale size. As distinct from the bag-of-feature based methods, we do not regard descriptor quantizations as ”visual words”. Instead a group of selected SIFT features of an object together with their spatial arrangement are represented by an attributed graph. Each graph is then regarded as a ”visual word

强行引入

膝盖

https://doi.org/10.1007/978-3-322-84070-7n to a solution that satisfies the visual as well as the structural constraints. An approach for rigid objects is presented and extended to handle articulated objects consisting of rigid parts. Experimental results on real and synthetic videos show promising results in scenes with considerable amount of occlusion.

conscience

Melodrama

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