Image Graph Matching Based on Region Adjacency Graph

Extraction of image features done in traditional pixel-based image analysis is not done effectively. This extraction is caused by extraction, which only represents content. A very promising and challenging approach is extracting graphs from images that represent not only content but also the relation of content. The process of graph extraction from an image begins with the segmentation process. The segmentation method using the Minimum Spanning Tree algorithm is one way to divide the image into homogeneous regions. The regions obtained in the segmentation stage are represented as vertices and relations between neighboring regions are represented as edges. This form of representation is called the Region Adjacency Graph (RAG). By assuming that the RAG obtained is a graph feature of the image, then a graph matching process is performed using it. Image graph matching is performed on the artificial images and batik core images using VF2 algorithm and Graph Edit Distance (GED) algorithm. Based on the experiments, the best evaluation results on artificial image data are obtained through the VF2 algorithm with an f-score value of 90.00%. Both precision and recall values for this algorithm have good values, namely 96.24% and 84.52%. Then in the batik core motif, the highest results are achieved through Graph Edit Distance with the GED ≥ 8 parameter. The f-score value obtained is 59.76%, and precision and recall are 47.46% and 60.00%