Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (12): 146-151.doi: 10.13475/j.fzxb.20190105306

• Comprehensive Review • Previous Articles     Next Articles

Research progress of fabric image feature extraction and retrieval based on convolutional neural network

SUN Jie1,2, DING Xiaojun1,3,4, DU Lei1,3,4, LI Qinman1, ZOU Fengyuan1,3,4()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. School of Design & Art, Communication University of Zhejiang, Hangzhou, Zhejiang 310018, China
    3. Zhejiang Provincial Research Center of Clothing Engineering Technology, Hangzhou, Zhejiang 310018, China
    4. Key Laboratory of Silk Culture Inheriting and Products Design Technology, Ministry of Culture and Tourism, Hangzhou, Zhejiang 310018, China
  • Received:2019-01-28 Revised:2019-07-23 Online:2019-12-15 Published:2019-12-18
  • Contact: ZOU Fengyuan E-mail:zfy166@zstu.edu.cn

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Abstract:

In order to explore the problem of automatic recognition and retrieval in fabric images, the research progress of this field in three aspects: shallow visual feature extraction, deep semantic feature representation and retrieval model construction was summarized, and then the existing problems were analyzed. The research shows that shallow visual features of fabric images have good applicability in the retrieval of small sample data sets, and the application of multi-feature fusion can effectively improve the retrieval accuracy, but there are limitations in the retrieval of large sample data sets and high-level semantic recognition. The deep convolutional neural network can effectively overcome this problem. The optimal design of fabric semantic attributes, the structural optimization of convolutional neural network and distance metric learning are three effective ways to improve the precision of deep retrieval model. It is believed that the improvement of fabric image recognition and retrieval accuracy in the future mainly depends on three aspects: accurate image segmentation and recognition technology, standardized semantic system design and multi-modal retrieval information fusion.

Key words: fabric image recognition, feature extract, image retrieval, convolutional neural network

CLC Number: 

  • TP181

Tab.1

Comparison of literatures on extraction of superficial features of fabrics"

文献 特征类别 指标/方法 样本个数 织物属性要素 是否涉及语义识别
[2] 颜色+形状特征 颜色直方图、Hu不变矩 300 纹样 否
[6] 颜色+全局特征 颜色矩、GIST特征 700 纹样 是
[8] 形状特征 离散余弦变换(DCT)特征 无 纹样 是
[13] 颜色+纹理特征 颜色直方图、LBP特征 200 风格 是
[14] 颜色+纹理特征 颜色直方图、分形纹理特征 200 表面肌理 是
[17] 纹理特征 灰度共生矩阵 无 组织结构 否
[19] 纹理特征 LBP特征 150 表面纹理 是
[22] 颜色+形态特征 三维CVQ特征、Hu不变矩 202 纹样 是
[24] 颜色+形态特征 Canny算子、颜色直方图 1 000 纹样 否

Fig.1

LeNet-5 CNN model structure"

Fig.2

Framework of fabric SBIR based on CNN"

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