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Author:

Liu, Yinyang (Liu, Yinyang.) | Xu, Xiaobin (Xu, Xiaobin.) | Li, Feixiang (Li, Feixiang.)

Indexed by:

CPCI-S

Abstract:

Deep learning have achieved great success both in image and natural language processing, When to search similar images, there are some data occur which image and image title are not related To deal with this problem which involves the process of both image and natural language, we propose a convolutional neural network model. The model both uses the feature of images and texts to judge the similarity. In the model, the two type of feature extracted respectively and then give the probability of the relationship between images and titles. This probability is added to the search strategy as a score to improve search quality.

Keyword:

natural language processing classification deep learning image processing

Author Community:

  • [ 1 ] [Liu, Yinyang]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 2 ] [Xu, Xiaobin]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
  • [ 3 ] [Li, Feixiang]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China

Reprint Author's Address:

  • [Xu, Xiaobin]Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China

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Source :

PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS)

ISSN: 2327-0594

Year: 2018

Page: 844-847

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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