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DFAEN: Double-order knowledge fusion and attentional encoding network for texture recognition

Yang, Zhijing, Lai, Shujian, Hong, Xiaobin, Shi, Yukai, Cheng, Yongqiang and Qing, Chunmei (2022) DFAEN: Double-order knowledge fusion and attentional encoding network for texture recognition. Expert Systems with Applications, 209. ISSN 0957-4174

Item Type: Article


Recent studies have shown that deep convolutional neural networks (CNNs) have been successfully used for texture representation and recognition. One of the most successful texture recognition methods is the deep texture encoding network (DeepTEN), which has been shown to be effective. However, this network directly uses redundant CNN features with generality and ignores the role of multiorder information during the encoding and learning processes. To address these issues, this paper proposes a double-order knowledge fusion and attentional encoding network for texture recognition (DFAEN). First, crucial texture features are encoded by an embedded attention mechanism. Second, double-order modeling is implemented in the encoding and learning stage to make full use of convolution feature information with different orders, enabling the network to focus on and learn more texture domain information. Our method can stably and effectively perform end-to-end optimization. Evaluation experiments conducted on several widely used benchmark datasets (e.g., the FMD, MINC-2500, the DTD, KTH-TISP-2b, and GTOS-mobile) show that our method clearly demonstrates superior performance to that of competing approaches.

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DFAEN- Double-Order Knowledge Fusion and Attentional Encoding Network for Texture Recognition.pdf - Accepted Version
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More Information

Depositing User: Yongqiang Cheng


Item ID: 16818
Identification Number:
ISSN: 0957-4174
Official URL:

Users with ORCIDS

ORCID for Yongqiang Cheng: ORCID iD

Catalogue record

Date Deposited: 21 Nov 2023 09:16
Last Modified: 21 Nov 2023 09:16


Author: Yongqiang Cheng ORCID iD
Author: Zhijing Yang
Author: Shujian Lai
Author: Xiaobin Hong
Author: Yukai Shi
Author: Chunmei Qing

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Computing > Artificial Intelligence

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