Texture Dataset Construction and Texture Image Retrieval based on Deep Learning
Authors: 
Zhisheng Zhang, Huaijing Qu, Hengbin Wang, Jia Xu, Jiwei Wang, Yanan WeiAuthors Info & Claims
Pages 105 - 111
Abstract
In the deep texture image retrieval, to address the problem that the retrieval performance is affected by the lack of sufficiently large texture image dataset used for the effective training of deep neural network, a deep learning based texture dataset construction and texture image retrieval method is proposed in this paper. First, a large-scale texture image dataset containing rich texture information is constructed based on the DTD texture image dataset, and used as the source dataset for pre-training deep neural networks. To effectively characterize the information of the source texture dataset, a revised version of the VGG16 model, called ReV-VGG16, is adaptively designed. Then, the pre-trained ReV-VGG16 model is combined with the target texture image datasets for the transfer learning, and the probability values of the output from the classification layer of the model are used for the computation of the similarity measurement to achieve the retrieval of the target texture image dataset. Finally, the retrieval experiments are conducted on four typical texture image datasets, namely, VisTex, Brodatz, STex and ALOT. The experimental results show that our method outperforms the existing state-of-the-art texture image retrieval approaches in terms of the retrieval performance.
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Index Terms
- Texture Dataset Construction and Texture Image Retrieval based on Deep Learning
Index terms have been assigned to the content through auto-classification.
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Published In
December 2021
437 pages
ISBN:9781450384155
DOI:10.1145/3507548
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Published: 09 March 2022
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- Refereed limited
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- Shandong Provincial Natural Science Foundation of China,
- Major Science and Technology Innovation Project of Shandong Province,
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CSAI 2021
CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence
December 4 - 6, 2021
Beijing, China
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