Horizon matching for localizing unordered panoramic images
Computer Vision and Image Understanding
Graph classification by means of Lipschitz embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A family of novel graph kernels for structural pattern recognition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A generic framework for median graph computation based on a recursive embedding approach
Computer Vision and Image Understanding
Speeding up graph edit distance computation through fast bipartite matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Discriminative prototype selection methods for graph embedding
Pattern Recognition
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Graph structures play a critical role in computer vision, but they are inconvenient to use in pattern recognition tasks because of their combinatorial nature and the consequent difficulty in constructing feature vectors. Spectral representations have been used for this task which are based on the eigensystem of the graph Laplacian matrix.However, graphs of different sizes produce eigensystems of different sizes where not all eigenmodes are present in both graphs. In this paper we use the Levenshtein distance to compare spectral representations under graph edit operations which add or delete vertices.The spectral representations are therefore of different sizes.We use the concept of the string-edit distance to allow for the missing eigenmodes and compare the correct modes toeach other.We evaluate the method by first using generated graphs to compare the effect of vertex deletion operations.We then examine the performance of the method on graphs from a shape database.