A Nectar of Frequent Approximate Subgraph Mining for Image Classification Un nectar sobre la minería de subgrafos frecuentes aproximados en clasificación de imágenes

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.