GFF-CARVING: Graph Feature Fusion for the Recognition of Highly Varying and Complex Balinese Carving Motifs

Abstract

The recognition of Balinese carving motifs is challenging due to the highly varying and interrelated motifs of Balinese carvings and in addition to the scantiness of Balinese carving data. This study proposed a method named GFF-CARVING for the recognition of Balinese carving motifs. GFF-CARVING is a deep learning architecture based on the Graph Convolutional Network (GCN) and Convolutional Neural Network (CNN) to extract image and graph features. GFF-CARVING applies feature fusion to improve the discriminative ability of the model to overcome these challenges and therefore improve its recognition performance. The proposed method consists of three main modules, the image representation learning module, the graph representation learning module, and the prediction module. The image representation learning module is based on ResNet and extracts the image features using global max pooling. The graph representation learning module is based on GCN and extracts the graph features. The graph features are handcrafted features that are built based on the occurrence relationship between the constituent sub-motifs of Balinese carvings. The feature fusion generates new features that take into account the occurrence relationship between the sub-motifs. These new features are used in the prediction module to accurately recognize the Balinese carving motifs. Based on the experimental results, GFF-CARVING achieved the highest recognition accuracy of 98.93% compared to other state-of-the-art models. These results indicated that feature fusion based on the handcrafted graph features and image features improved the discriminative ability of GFF-CARVING in recognizing Balinese carving motifs.

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