Cognitive Garment Panel Design Based on BSG Representation and Matching

Previously, the fashion industry and apparel manufacturing have been applying intelligent CAD technologies with sketching interfaces to operate garment panel shapes in digital form. The authors propose a novel bi-segment graph BSG representation and matching approach to facilitate the searching of panel shapes for sketch-based cognitive garment design and recommendation. First in the front-tier, they provide a sketching interface for designers to input and edit the clothing panels. A panel shape is then decomposed into a sequence of connected segments and represented by the proposed BSG model to encode its intrinsic features. A new matching metric based on minimal spanning tree is also proposed to compute the similarity between two BSG models. The simulation of the resulting garment design is also visualized and returned to the user in 3D. Experiment results show the effectiveness and efficiency of the proposed method.

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