Abstract
The spectral graph theories have been widely used in the domain of image clustering where editing distances between graphs are critical. This paper presents a method for spectral edit distance between the graphs constructed on the images. Using the feature points of each image, we define a weighted adjacency matrix of the relational graph and obtain a covariance matrix based on the spectra of all the graphs. Then we project the vectorized spectrum of each graph to the eigenspace of the covariance matrix, and derive the distances between pairwise graphs. We also conduct some theoretical analyses to support our method. Experiments on both synthetic data and real-world images demonstrate the effectiveness of our approach.
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Wang, N., Tang, J., Zhang, J., Fan, YZ., Liang, D. (2007). Spectral Edit Distance Method for Image Clustering. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_37
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DOI: https://doi.org/10.1007/978-3-540-72524-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
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