For sample reproduction, texture and color are both significant when the consumer has no specific or individual demands or cannot describe the requirements clearly. In this paper, an effective method based on aggregated convolutional descriptors and approximate nearest neighbors search was proposed to combine the texture and color feature for wool fabric retrieval. Aggregated convolutional descriptors from different layers were combined to characterize the wool fabric image. The approximate nearest neighbors search method Annoy was adopted for similarity measurement to balance the trade-off between the search performance and the elapsed time. A wool fabric image database containing 82,073 images was built to demonstrate the efficacy of the proposed method. Different feature extraction and similarity measurement methods were compared with the proposed method. Experimental results indicate that the proposed method can combine the texture and color feature, being effective and superior for image retrieval of wool fabric. The proposed scheme can provide references for the worker in the factory, saving a great deal of labor and material resources.

对于样品复制，当消费者没有特定或个性化的需求或无法清楚地描述需求时，质地和颜色都很重要。本文提出了一种基于聚合卷积描述符和近似最近邻搜索的有效方法，结合纹理和颜色特征进行羊毛织物检索。来自不同层的聚合卷积描述符被组合来表征羊毛织物图像。采用近似最近邻搜索方法 Annoy 进行相似度测量，以平衡搜索性能和经过时间之间的权衡。建立了一个包含 82,073 张图像的羊毛织物图像数据库来证明所提出方法的有效性。将不同的特征提取和相似度测量方法与所提出的方法进行了比较。实验结果表明，所提出的方法可以结合纹理和颜色特征，对羊毛织物的图像检索是有效且优越的。所提出的方案可为厂内工人提供参考，节省大量人力物力。