A Nectar of Frequent Approximate Subgraph Mining for Image Classification

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

Resumen


Frequent approximate subgraph mining has emerged as an important research topic where graphs are used for modeling entities and their relations including some distortions in the data. In the last years, there has been a considerable growth in the application of this kind of mining on image classification; achieving competitive results against other approaches. In this nectar, a review of recent contributions on image classification based on frequent approximate subgraph mining is presented. We highlight the usefulness of this type of mining, as well as the improvements achieved in terms of efficiency and efficacy of the proposed frameworks.

Palabras clave


approximate graph mining; frequent approximate subgraph mining; graph-based image classification

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