Due to the potential value in many areas, such as e-commerce and inventory management, fabric image retrieval, which is a special case of content-based image retrieval, has recently become a research hotspot. As a major category of textile fabrics, patterned fabrics have a diverse and complex appearance, making the retrieval task more challenging. To address this situation, this paper proposes a novel approach for patterned fabric based on the non-subsampled contourlet transform (NSCT) feature descriptor and relevance feedback technique. To integrate the color information into the NSCT feature descriptor, we extract the feature of patterned fabric images in HSV color space. An outlier rejection-based parametric relevance feedback algorithm is employed to adjust the similarity matrix to improve the retrieval results. The experimental results not only show the effectiveness of the proposed approach but also demonstrate that it can significantly improve the performance of the retrieval system compared to other state-of-the-art algorithms.

由于在电子商务和库存管理等许多领域的潜在价值，织物图像检索作为基于内容的图像检索的特例，最近成为研究热点。花纹织物作为纺织面料的一大类，外观多样且复杂，使得检索任务更具挑战性。为了解决这种情况，本文提出了一种基于非下采样轮廓波变换 (NSCT) 特征描述符和相关反馈技术的图案织物的新方法。为了将颜色信息整合到 NSCT 特征描述符中，我们在 HSV 颜色空间中提取图案织物图像的特征。采用基于异常值拒绝的参数相关性反馈算法调整相似度矩阵以改善检索结果。