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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 3
951-956

Fusion of multi-level image features for image-text matching

Hao Zhifeng1,2
Li Junfeng1
Cai Ruichu1
Wen Wen1
Wang Lijuan1
Li Yiting1
1. College of Computer, Guangdong University of Technology, Guangzhou 510006, China
2. College of Mathematics & Big Data, Foshan University, Foshan Guangdong 528000, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0780

Abstract

The existing mainstream methods use the pre-trained convolutional neural networks to extract image features and usually have the following limitations: a) only using a single layer of pre-trained features to represent image; b) the pre-trained task is inconsistent with the actual research task. These limitations result in that the existing methods of image-text matching cannot make full use of image features and is easily influenced by the noises. To solve the above limitations, this paper used multi-layer features from a pre-trained network and proposed a fusion algorithm of multi-level image features accordingly. Under the guidance of the image-text matching objective function, the proposed algorithm fused the multi-level pre-trained image features and reduced their dimensionality using a multi-layer perceptron to generate fusion features. It was able to make full use of pre-trained features and successfully reduce the influences of noises. The experimental results show that the proposed fusion algorithm makes better use of pre-trained image features and outperforms the methods using single-level features in the image-text matching task.

Foundation Support

NSFC-广东联合基金资助项目(U1501254)
国家自然科学基金资助项目(61472089)
广东省自然科学基金资助项目(2014A030306004,2014A030308008)
广东省科技计划资助项目(2015B010108006,2015B010131015)
广东省特支计划资助项目(2015TQ01X140)
广州市珠江科技新星项目(201610010101)
广州市科技计划资助项目(201604016075)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0780
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Technology of Graphic & Image
Pages: 951-956
Serial Number: 1001-3695(2020)03-070-0951-06

Publish History

[2020-03-05] Printed Article

Cite This Article

郝志峰, 李俊峰, 蔡瑞初, 等. 面向图文匹配任务的多层次图像特征融合算法 [J]. 计算机应用研究, 2020, 37 (3): 951-956. (Hao Zhifeng, Li Junfeng, Cai Ruichu, et al. Fusion of multi-level image features for image-text matching [J]. Application Research of Computers, 2020, 37 (3): 951-956. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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