Objective evaluation of fabric pilling based on data-driven visual attention model

The purpose of this paper is to develop a new objective evaluation method of fabric pilling using data-driven visual attention model.

First, the multi-scale filtering images are formed by Gaussian pyramid decomposition. Second, center-surround differences algorithm is used between multi-scale filtering images to build saliency map. On this basis, the pilling information is segmented from saliency map by the segmentation threshold. Finally, the pilling is objectively evaluated by extracting pilling feature. Experimental result shows that compared with the traditional detection methods, the proposed objective evaluation method has strong anti-interference ability, and correct classification rate (CCR) is 96 percent.

Fabric pilling saliency can be effectively improved by data-driven visual attention model, which will lead to stronger anti-interference ability and higher correct classification rate.

To void uneven illumination, noise, and texture interference, the proposed method can enhance the saliency of small targets in saliency map using a bottom-up visual attention model. Through the threshold segmentation according to pilling feature, the pilling information is effectively from the fabric texture. Pilling feature about pilling area density is extracted to pilling grade evaluation.