Textile Image Segmentation through Region Action Graph and Novel Region Merging Strategy

Textile image segmentation is widely used in textile industry design, since users often need to reconstruct and redesign the patterns of the textile image. Different from traditional image segmentation methods, this paper focused on handling textile images, which received little attention until now. Taking into account the characteristics of textile, this paper proposed a novel graph theory and region merging strategy based textile image segmentation method. Our method first generated the over-segmented image by applying the graph-based image segmentation on the original image. Then we extracted the predominant color to mark the background segments. The region action graph was proposed to improve the conventional region adjacency graph before building the region relation graph for the following region merging. It can greatly improve the segmentation quality since textile image usually includes the regions with complex distribution of different colors. In the phase of region merging, we formulated it as designing merging criterions for the relate regions with geometry properties, such as globalist, locality, and spatial continuity. Extensive experiments were performed and the results showed that our method can reliably segment the textile images into sections with perceptual meaning. Additionally, our method is simple and efficient, with great potential in practical applications.