A novel image retrieval strategy based on transfer learning and hand-crafted features for wool fabric

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 82,073 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.