We present a technical demonstration of a world-scale touristic landmark recognition engine. To build such an engine, we leverage ~21.4 million images, from photo sharing websites and Google Image Search, and around two thousand web articles to mine the landmark names and learn the visual models. The landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. This demonstration gives three exhibits: (1) a live landmark recognition engine that can visually recognize landmarks in a given image; (2) an interactive navigation tool showing landmarks on Google Earth; and (3) sample visual clusters (landmark model images) and a list of 1000 randomly selected landmarks from our recognition engine with their iconic images.

References

[1]

J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1):107--113, 2008.

Digital Library

Google Scholar

[2]

D. G. Lowe. Object recognition from local scale-invariant features. In ICCV 99, pages 1150--1157, 1999.

Digital Library

Google Scholar

[3]

Y.-T. Zheng, M. Zhao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T.-S. Chua, and H. Neven. Tour the world: building a web-scale landmark recognition engine. In Proceedings of International Conference on Computer Vision and Pattern Recognition, Miami, Florida, U.S.A, June, 2009.

Crossref

Google Scholar

Cited By

View all

Yin YZhang YLiu ZWang SShah RZimmermann R(2022)GPS2Vec: Pre-Trained Semantic Embeddings for Worldwide GPS CoordinatesIEEE Transactions on Multimedia10.1109/TMM.2021.306095124(890-903)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3060951
Qian XWu YLi MRen YJiang SLi Z(2021)LAST: Location-Appearance-Semantic-Temporal Clustering Based POI SummarizationIEEE Transactions on Multimedia10.1109/TMM.2020.297747823(378-390)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2977478
Zemene ETesfaye YIdrees HPrati APelillo MShah M(2019)Large-Scale Image Geo-Localization Using Dominant SetsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.278713241:1(148-161)Online publication date: 1-Jan-2019
https://doi.org/10.1109/TPAMI.2017.2787132
Show More Cited By

Index Terms

Tour the world: a technical demonstration of a web-scale landmark recognition engine
1. Applied computing
  1. Operations research
    1. Consumer products

Recommendations

How are we searching the world wide web?: a comparison of nine search engine transaction logs
Special issue: Formal methods for information retrieval

The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five ...
Search Engines for the World Wide Web
Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face ...

Comments

Information & Contributors

Information

Published In

MM '09: Proceedings of the 17th ACM international conference on Multimedia

October 2009

1202 pages

ISBN:9781605586083

DOI:10.1145/1631272

General Chairs:
Wen Gao
Peking University, China
,
Yong Rui
Microsoft, China
,
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Program Chairs:
Changsheng Xu
Institute of Automation, Chinese Academy of Sciences, China
,
Eckehard Steinbach
Technical University of Munich, Germany
,
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Michelle Zhou
IBM T. J. Watson Research Center, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

touristic landmark recongition

Qualifiers

Demonstration

Conference

MM09

Sponsor:

SIGMM

MM09: ACM Multimedia Conference

October 19 - 24, 2009

Beijing, China

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

84
Total Citations
View Citations
444
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yin YZhang YLiu ZWang SShah RZimmermann R(2022)GPS2Vec: Pre-Trained Semantic Embeddings for Worldwide GPS CoordinatesIEEE Transactions on Multimedia10.1109/TMM.2021.306095124(890-903)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3060951
Qian XWu YLi MRen YJiang SLi Z(2021)LAST: Location-Appearance-Semantic-Temporal Clustering Based POI SummarizationIEEE Transactions on Multimedia10.1109/TMM.2020.297747823(378-390)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.2977478
Zemene ETesfaye YIdrees HPrati APelillo MShah M(2019)Large-Scale Image Geo-Localization Using Dominant SetsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.278713241:1(148-161)Online publication date: 1-Jan-2019
https://doi.org/10.1109/TPAMI.2017.2787132
Jiang SKong YFu Y(2019)Deep Geo-Constrained Auto-Encoder for Non-Landmark GPS EstimationIEEE Transactions on Big Data10.1109/TBDATA.2017.27730965:2(120-133)Online publication date: 1-Jun-2019
https://doi.org/10.1109/TBDATA.2017.2773096
Chen JDuan YSobelman GZhang C(2019)Memory Segment Matching Network Based Image Geo-LocalizationIEEE Access10.1109/ACCESS.2019.29223787(77448-77459)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2922378
Nguyen VNgo TTran MLe DDuong D(2018)A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image RetrievalComputer Vision10.4018/978-1-5225-5204-8.ch054(1307-1321)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5204-8.ch054
Yu JKuang ZZhang BZhang WLin DFan J(2018)Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image SharingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.278798613:5(1317-1332)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TIFS.2017.2787986
Zhang XWang SLi ZMa S(2018)Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier LearningIEEE Transactions on Cybernetics10.1109/TCYB.2017.271279848:6(1682-1695)Online publication date: Jun-2018
https://doi.org/10.1109/TCYB.2017.2712798
Qian XWang HZhao YHou XHong RWang MTang Y(2017)Image Location Inference by Multisaliency EnhancementIEEE Transactions on Multimedia10.1109/TMM.2016.263820719:4(813-821)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TMM.2016.2638207
Wen YYeo JPeng WHwang S(2017)Efficient Keyword-Aware Representative Travel Route RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.269042129:8(1639-1652)Online publication date: 1-Aug-2017
https://doi.org/10.1109/TKDE.2017.2690421
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

How are we searching the world wide web?: a comparison of nine search engine transaction logs

Search Engines for the World Wide Web

Open-set face recognition across look-alike faces in real-world scenarios