A novel retrieval strategy for colored spun yarn fabrics based on hand-crafted features and support vector machine

Abstract

The traditional retrieval methods of colored spun yarn fabrics are low in efficiency and accuracy. To solve this problem, this paper proposed a novel retrieval strategy for colored spun yarn fabrics based on hand-crafted features and support vector machine (SVM). First, the SVM classifier was built to automatically classify fabric images. Second, different texture and color features were extracted for solid-color fabrics and stylized fabrics based on hand-crafted feature extraction methods. Third, the retrieval strategy was formulated to realize image retrieval of colored spun yarn fabrics based on the output probability of the predicted sample category. The image database which contains over 6000 images was built to verify the proposed method. Experiments show that the mAP of the proposed method reaches 0.837 and the elapsed time is 0.171 s. Results indicate that the proposed method is feasible and effective, being superior to other methods for image retrieval of colored spun yarn fabrics. The proposed method can provide references for the production crew in the factory to shorten the production cycle and reduce manual labor.