Diversifying landmark image search results by learning interested views from community photos

Yuheng Ren, Mo Yu, Xin-Jing Wang, Lei Zhang, Wei-Ying Ma

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

14 Citations (Scopus)

Abstract

In this paper, we demonstrate a novel landmark photo search and browsing system: Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate the user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and set-based ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and users' preference for Agate. © 2010 International World Wide Web Conference Committee (IW3C2).
Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Pages1289-1292
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: 26 Apr 201030 Apr 2010

Publication series

NameProceedings of the 19th International Conference on World Wide Web, WWW '10

Conference

Conference19th International World Wide Web Conference, WWW2010
PlaceUnited States
CityRaleigh, NC
Period26/04/1030/04/10

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

  • landmark image search
  • set-based ranking
  • user interest modeling

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