Wool fabric image retrieval based on soft similarity and listwise learning. Issue 21 (November 2022)

• Record Type: Journal Article
• Authors: Xiang, Jun; Zhang, Ning; Pan, Ruru; Gao, Weidong
• Is Part Of: Textile research journal. Volume 92:Issue 21/22(2022)
• Page Start: 4470
• Page End: 4483
• Publication Date: 2022-11
• Subjects: Content-based image retrieval -- fabric retrieval -- deep hashing -- listwise learning -- similarity learning; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175221102636
• Languages: English
• ISSNs: 0040-5175

Abstract

As a special case in content-based image retrieval, fabric retrieval has high potential application value in many fields. However, fabric retrieval has higher requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. It is also a challenging issue with several obstacles: variety and complexity of fabric appearance, and high requirements for retrieval accuracy. To address this issue, this paper presents a novel method for fabric image retrieval based on soft similarity and pairwise learning. First, a soft similarity between two fabric images is defined to describe their relationship. Then, a convolutional neural network with compact structure and cross-domain connections is designed to learn the fabric image representation. Finally, listwise learning is introduced to train the convolutional neural network model and hash function. The generated hash codes are used to index the fabric image. The experiments are conducted on a wool fabric dataset. The experimental results show that the newly proposed method has a greater improvement than our previous work.