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Author:

Yang, Ying (Yang, Ying.) | Zhang, Jing (Zhang, Jing.) | Liu, Jihong (Liu, Jihong.) | Li, Jiafeng (Li, Jiafeng.) | Zhuo, Li (Zhuo, Li.)

Indexed by:

CPCI-S

Abstract:

The tags are usually tagged by different users in social image sharing websites, which can indicate image semantic information and imply user's preference. Therefore, the tags can contribute to personalized recommendation of social image. However, the present social image tags models only consider single tag, resulting in the relationships among tags are ignored. In this paper, we propose a novel method to create tag tree of social image for personalized recommendation. Firstly, the tag ranking is realized to remove noisy tags. Then, the first layer tags are selected from re-ranked tags lists. To sufficiently express tag's significances, the tag subtrees can be created based on different image categories and combined with first layer tags to create tag tree. Finally, the personalized recommendation of social image is achieved by using tag tree. Experimental results show that our tag tree can effectively express the relationships among tags as well as obtain satisfactory results in personalized recommendation of social image.

Keyword:

Social image tag ranking personalized recommendation tag tree co-occurrence

Author Community:

  • [ 1 ] [Yang, Ying]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 2 ] [Zhang, Jing]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 3 ] [Liu, Jihong]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 4 ] [Li, Jiafeng]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 5 ] [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 6 ] [Zhuo, Li]Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing, Peoples R China

Reprint Author's Address:

  • [Yang, Ying]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

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Source :

2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)

ISSN: 1522-4880

Year: 2017

Page: 2164-2168

Language: English

Cited Count:

WoS CC Cited Count: 14

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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