Abstract
This paper deals with the classification of multiple pattern observations sets. A set of observations consists of different transformations, possibly including rotation, perspectives and projections. Each set belongs to a single pattern, that is, the pattern is considered invariant under such transformations. The method uses a network representation of the input data to take advantage of the topological relations between the patterns revealed by a low-dimensional manifold. A measurement called modularity is computed to numerically indicate the topological characteristics of the constructed networks. Simulations were carried out in real image data sets, and results have showed that the proposed method outperforms some recent and state-of-the-art techniques.
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The authors would like to acknowledge the São Paulo State Research Foundation (FAPESP) and the Brazilian National Council for Scientific and Technological Development (CNPq) for the financial support given to this research.
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Cupertino, T.H., Silva, T.C. & Zhao, L. Classification of multiple observation sets via network modularity. Neural Comput & Applic 23, 1923–1929 (2013). https://doi.org/10.1007/s00521-012-1115-y
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DOI: https://doi.org/10.1007/s00521-012-1115-y