| Shen Fei(沈飞)*,Wei Mengwan*,Liu Jiajun*,Zeng Huanqiang**,Zhu Jianqing*.[J].高技术通讯(英文),2020,26(2):196~203 |
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| RGB and LBP-texture deep nonlinearly fusion features for fabric retrieval |
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| DOI：doi：10.3772/j.issn.1006-6748.2020.02.009 |
| 中文关键词: |
| 英文关键词: fabric retrieval, feature fusion, convolutional neural network(CNN) |
| 基金项目: |
| Author Name | Affiliation | | Shen Fei(沈飞)* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China) | | Wei Mengwan* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China) | | Liu Jiajun* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China) | | Zeng Huanqiang** | (**School of Information Science and Engineering, Huaqiao University, Xiamen 361021, P.R.China) | | Zhu Jianqing* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Fabric retrieval is very challenging since problems like viewpoint variations, illumination changes, blots, and poor image qualities are usually encountered in fabric images. In this work, a novel deep feature nonlinear fusion network (DFNFN) is proposed to nonlinearly fuse features learned from RGB and texture images for improving fabric retrieval. Texture images are obtained by using local binary pattern texture (LBP-Texture) features to describe RGB fabric images. The DFNFN firstly applies two feature learning branches to deal with RGB images and the corresponding LBP-Texture images simultaneously. Each branch contains the same convolutional neural network (CNN) architecture but independently learning parameters. Then, a nonlinear fusion module (NFM) is designed to concatenate the features produced by the two branches and nonlinearly fuse the concatenated features via a convolutional layer followed with a rectified linear unit (ReLU). The NFM is flexible since it can be embedded in different depths of the DFNFN to find the best fusion position. Consequently, DFNFN can optimally fuse features learned from RGB and LBP-Texture images to boost the retrieval accuracy. Extensive experiments on the Fabric 1.0 dataset show that the proposed method is superior to many state-of-the-art methods. |
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