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Improving graph-based image classification by using emerging patterns as attributes

2016; Elsevier BV; Volume: 50; Linguagem: Inglês

10.1016/j.engappai.2016.01.030

1873-6769

Niusvel Acosta-Mendoza, Andrés Gago-Alonso, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad, José E. Medina-Pagola,

Graph Theory and Algorithms

In recent years, frequent approximate subgraph (FAS) mining has been used for image classification. However, using FASs leads to a high dimensional representation. In order to solve this problem, in this paper, we propose using emerging patterns for reducing the dimensionality of the image representation in this approach. Using our proposal, a dimensionality reduction over 50% of the original patterns is achieved, additionally, better classification results are obtained.