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

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. HighlightsWe combine FASs together with emerging patterns for image classification.To the best of our knowledge, this is the first work that proposes such combination.A dimensionality reduction of over 50% of the original patterns is achieved.Improvements on classification results are achieved.

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