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Fine-texture Image Retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. However, there are very few researches focus on this special case. Due to the difference between fine-texture images and common images, general retrieval methods for common images are difficult to apply to fine-texture image retrieval. It is also a challenging issue with several obstacles: variety and complexity of appearance, as well as high requirements for retrieval accuracy. To address this issue, this study proposes a novel approach, called deep multi-view hashing (DMVH), to learn enhanced hash codes for efficient fine-texture image retrieval. We propose to use the first few layers of a deep convolutional neural network for fine-texture image presentation. To avoid overfitting, we employ L0Linear layer instead of the commonly used Linear layer in all the fully connected layers. Then we introduce a pairwise quantified similarity computed on the semantic labels. And the learning process of the model is guided by multiple labels of images. This study takes fabric as an example, and builds a dataset called MFT-fabric-v1, to validate the effectiveness of the DMVH method. On this data set, the proposed model achieved a performance with a mAP value of o.8735. The area under the pr curve is larger than other comparison methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art on the MFT-fabric-v1 dataset.

The application of image retrieval techniques in industrial settings aims at rapid and accurate retrieval of the same or similar products from an archive to facilitate the production process. Content-based image retrieval and its applications on fabrics focused on the appearance differences and ignored the delicate differences, thus the retrieval fineness cannot meet the demands of industrial applications. In this study, a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features. The pre-trained model based on ImageNet was fine-tuned to extract features for automatic classification. The images in the wool fabric image database were classified into different categories. Meanwhile, the distribution probabilities were used to build the retrieval strategy. To form the ‘feature database’ in each category, oriented FAST and rotated BRIEF (ORB) was utilized for feature extractions and the vector of locally aggregated descriptors was adopted for feature aggregation. Ball tree was implemented to search the nearest neighbors for the final results. Based on the above methods, the query image was first classified automatically, and then the images were retrieved based on the retrieval strategy. A large-scale image database of wool fabrics with 82073 images was built as the benchmark to evaluate the efficacy of the proposed method. Experiments indicated that the proposed strategy is effective for delicate image retrieval. The combination of transfer learning and hand-crafted features can discriminate the appearance and delicate differences, being superior to the existing methods on representing the delicate fabric differences. The proposed method can provide referential assistance for the production crew and reducing manual labor in the factory.

Evaluation of fabric shape retention is an indispensable part of fabric performance evaluation. The existing evaluation methods for investigating the wrinkle recovery, drape, stiffness of fabrics are very mature, but a comprehensive and intuitive evaluation of fabric shape retention is not achieved. Based on the previously proposed fabric shape retention testing device and image processing method, this paper proposes a method to fit fabric crease contour lines to Gaussian curves to optimize the fabric crease contour lines and describe the fabric crease shape more intuitively. The indexes, which are the vertex angle (VA), the vertex height (VH), the maximum curvature (MC), and the enclosed area (EA), can be extracted from the Gaussian curves of the stable state to characterize different shape retention properties of the fabric. By comparing the test results of the standard method, it was found that VA and MC can form a linear regression relationship with the crease recovery angle to describe the crease recovery property of the fabric. VH, MC and EA have linear regression relationships with static drape coefficient or bending length to describe the drape and stiffness of the fabric. Compared with previous studies, this article enables to inspect the fabric crease recovery process dynamically, depict the crease recovery shape visually and evaluate the fabric shape retention performance comprehensively and accurately. This paper provides an experimental basis for investigating the evaluation method of fabric shape retention performance.

In calendering process, the fabric undergoes luster treatment to make the surface uniformly glossy at a high operation speed. It is easy to be misjudged for the seam detection by human vision in the process, especially for the seam detection of light fabric. To improve quality products and revenues, effective and fast feature extraction algorithms are urgently needed to solve the problem. This paper presents a new wavelet energy measure method and also compares it with a novel scheme for automated textural feature extraction implementation in CIELAB color space. In wavelet-based approach, energy measures are extracted in the best number decomposition. In the CIELAB color space, we proposed to calculate characteristic parameters included mean, standard deviation and variation coefficient (CV). Then the feature data obtained from the two approaches are analyzed to determine optimized threshold values in order to recognize seam information. Compared the detection results of the two algorithms, the characteristic parameters extraction in the CIELAB color space shows higher accuracies and more computationally efficient than wavelet-based approach on detecting fabric seam in calendering process.