Fabric image retrieval is a meaningful issue, due to its potential values in many areas such as textile product design, e-commerce, and inventory management. Meanwhile, it is challenging because of the diversity of fabric appearance. Encourage by the recent breakthrough in the deep convolutional neural network (CNN), a deep learning framework is applied for fabric image retrieval. The idea of the proposed framework is that the binary code and feature for representing the image can be learning by a deep CNN when the data labels are available. The proposed framework employs a hierarchical search strategy that includes coarse-level retrieval and fine-level retrieval. Otherwise, a large-scale wool fabric image retrieval dataset named WFID with about 20 000 images are built to validate the proposed framework. The longitudinal comparison experiments for self-parameter optimization and horizontal comparison experiments for verifying the superiority of the algorithm are performed on this data set. The comparison experimental results indicate the superiority of the proposed framework. INDEX TERMS Image retrieval, wool, fabric, feature extraction, machine learning, neural networks.

With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

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Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existing products and reduce the work of color measurement in sample analysis, this paper proposes an effective method based on dominant colors (DCs) and color moments (CMs) for wool fabric image retrieval. Firstly, the image was scaled to reduce computational time. Then, the hue, saturation, value color space was divided into 128 parts by the fast color quantization algorithm to extract the DCs of the image. Meanwhile, the CMs based on image partition were calculated in CIE L* a* b* color space to describe the spatial color information. Subsequently, different similarity measure methods were carried out based on the DC feature and CM feature. Finally, experiments were conducted on a wool fabric image database with 20,000 images for parameter optimization and verification. The average precision and recall were up to 87% and 44%, respectively. Experimental results show that the proposed scheme can retrieve images with the same or similar colors quickly and effectively and it outperformed other methods, providing referential assistance for the factory worker when retrieving wool fabrics.