Implementasi Pengelompokan Citra Batik dengan Pendekatan Metode Graph-Based Multi-View Clustering
-
Budi Hartono
*
Universitas Stikubank, Semarang,
Indonesia
- Veronica Lusiana Universitas Stikubank, Semarang, Indonesia (*) Corresponding Author
Abstract
Batik is a cultural heritage of Indonesia characterized by a rich array of motifs, colors, geometric patterns, and complex textures. This visual complexity makes Batik images compelling subjects for analysis using image processing and machine learning approaches, particularly for unsupervised clustering. A primary challenge in this research is effectively representing Batik's visual characteristics so pattern similarities can be accurately identified. Three texture features contrast, entropy, and energy serve as distinct "views" of Batik characteristics. These feature values are obtained by extracting them using the Gray-Level Co-occurrence Matrix (GLCM) method. The study utilizes a Graph-based Multi-View Clustering approach to group Batik images based on inter-image similarity. The study constructs a combined consensus graph with 30 nodes and 219 edges by integrating contrast, energy, and entropy features. This graph exhibits a density of 0.503 and forms a single connected graph component. The density result indicates that 50.3% of all possible image pairs share a connection. The resulting graph forms a connected structure that incorporates information from all three views, yielding a more comprehensive clustering representation than any single view. The combined consensus graph establishes an interconnected structure that produces a representative clustering of Batik images.
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