Abstract
We introduce a method for computing similarity between two square matrices based on the information given by their eigenvalues and eigenvectors. The idea is to evaluate the effect of the conjugation of the original matrices and the eigenvectors and eigenvalues of each other. Then, we exemplify its utility for computing similarity between square images from a classified bank of pictures. The performance of the method is evaluated with diverse experiments.
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Ortega-González, V., Angeles-Yreta, A., Medina-Apodaca, J., Landassuri-Moreno, V., Figueroa-Nazuno, J. (2006). Eigenconjugation: An Approach for Computing Image Similarity. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_24
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DOI: https://doi.org/10.1007/11892755_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46556-0
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