skip to main content
10.1145/1460096.1460099acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Mining GPS traces and visual words for event classification

Published: 30 October 2008 Publication History

Abstract

It is of great interest to recognize semantic events (e.g., hiking, skiing, party), in particular when given a collection of personal photos, where each photo is tagged with a timestamp and GPS (Global Positioning System) information at the capture. We address this emerging multiclass classification problem by mining informative features derived from traces of GPS coordinates and a bag of visual words, both based on the entire collection as opposed to individual photos. Considering that semantic events are best characterized by a compositional description of the visual content in terms of the co-occurrence of objects and scenes, we focus on mining compositional features (equivalent to word combinations in the "bag-of-words" method) that have better discriminative and descriptive abilities than individual features. In order to handle the combinatorial complexity in discovering such compositional features, we apply a data mining method based on frequent itemset mining (FIM). Complementary features are also derived from GPS traces and mined to characterize the underlying movement patterns of various event types. Upon compositional feature mining, we perform multiclass AdaBoost to solve the multiclass problem. Based on a dataset of eight event classes and a total of more than 3000 geotagged images from 88 events, experimental results using leave-one-out cross validation have shown the synergy of all of the components in our proposed approach to event classification.

References

[1]
L. Cao, J. Luo, H. Kautz, and T. Huang. Annotating collections of photos using hierarchical event and scene models. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2008.
[2]
H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative frequent pattern analysis for effective classification. In Proc. of Intl. Conf. on Data Engineering 2007.
[3]
H. Cheng, X. Yan, J. Han, and P. S. Yu. Direct discriminative pattern mining for effective classification. In Proc. of Intl. Conf. on Data Engineering 2008.
[4]
S. Ebadollahi, L. Xie, S.-F. Chang, and J. R. Smith. Visual event detection using multi-dimensional concept dynamics. In Proc. IEEE Conf. on Multimedia Expo 2006.
[5]
G. Grahne and J. Zhu. Fast algorithms for frequent itemset mining using fp-trees. IEEE Transaction on Knowledge and Data Engineering 2005.
[6]
J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: current status and future directions. In Data Mining and Knowledge Discovery 2007.
[7]
L. Kennedy, M. Naaman, S. Ahern, R. Nair, and T. Rattenbury. How flickr helps us make sense of the world: Context and content in community-contributed media collections. In Proc. ACM Multimedia 2007.
[8]
S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features:spatial pyramid matching for recognizing natural scene categories. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2006.
[9]
L.-J. Li and L. Fei-Fei. What, where and who? classifying events by scene and object recognition. In Proc. IEEE Intl. Conf. on Computer Vision 2007.
[10]
B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proc. SIGKDD 1998.
[11]
D. Liu, G. Hua, P. Viola, and T. Chen. Integrated feature selection and higher-order spatial feature extraction for object categorization. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2008.
[12]
D. Lowe. Distinctive image features from scale-invariant keypoints. Intl. Journal of Computer Vision 2004.
[13]
J. Luo and M. Boutell. Automatic image orientation detection via confidence-based integration of low-level and semantic cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(5):715--726, 2005.
[14]
T. Mitchell. Machine Learning McGraw Hill, 1997.
[15]
X. Yin and J. Han. Cpar: classification based on predictive association rules. In SIAM International Conference data mining (SDM)2003.
[16]
J. Yuan, J. Luo, and Y. Wu. Mining compositional features for boosting. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2008.
[17]
J. Yuan, Y. Wu, and M. Yang. Discovery of collocation patterns:from visual words to visual phrases. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2007.
[18]
J. Yuan, Y. Wu, and M. Yang. From frequent itemsets to semantically meaningful visual patterns. In Proc. ACM SIGKDD 2007.
[19]
J. Zhu, S. Rosset, H. Zou, and T. Hastie. Multi-class adaboost. Technique Report 2005.

Cited By

View all
  • (2020)Location Sensitive Image Retrieval and TaggingComputer Vision – ECCV 202010.1007/978-3-030-58517-4_38(649-665)Online publication date: 10-Oct-2020
  • (2016)Abnormal Event Discovery in User Generated PhotosProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2975215(47-51)Online publication date: 1-Oct-2016
  • (2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrieval
October 2008
506 pages
ISBN:9781605583129
DOI:10.1145/1460096
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. event categorization
  2. gps information
  3. image data mining

Qualifiers

  • Research-article

Conference

MM08
Sponsor:
MM08: ACM Multimedia Conference 2008
October 30 - 31, 2008
British Columbia, Vancouver, Canada

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2020)Location Sensitive Image Retrieval and TaggingComputer Vision – ECCV 202010.1007/978-3-030-58517-4_38(649-665)Online publication date: 10-Oct-2020
  • (2016)Abnormal Event Discovery in User Generated PhotosProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2975215(47-51)Online publication date: 1-Oct-2016
  • (2015)[Invited Paper] A Review of Web Image MiningITE Transactions on Media Technology and Applications10.3169/mta.3.1563:3(156-169)Online publication date: 2015
  • (2015)Multi-Query Augmentation-Based Web Landmark Photo RetrievalThe Computer Journal10.1093/comjnl/bxv03358:9(2120-2134)Online publication date: 15-May-2015
  • (2014)A scalable algorithm for extraction and clustering of event-related picturesMultimedia Tools and Applications10.1007/s11042-012-1087-z70:1(55-88)Online publication date: 1-May-2014
  • (2013)Event Recognition in Photo Collections with a Stopwatch HMMProceedings of the 2013 IEEE International Conference on Computer Vision10.1109/ICCV.2013.151(1193-1200)Online publication date: 1-Dec-2013
  • (2012)ReferencesMultimedia Information Extraction10.1002/9781118219546.refs(425-460)Online publication date: 24-Aug-2012
  • (2011)Automatic image semantic interpretation using social action and tagging dataMultimedia Tools and Applications10.1007/s11042-010-0650-851:1(213-246)Online publication date: 1-Jan-2011
  • (2011)Geotagging in multimedia and computer vision--a surveyMultimedia Tools and Applications10.1007/s11042-010-0623-y51:1(187-211)Online publication date: 1-Jan-2011
  • (2010)SNDocRankProceedings of the international conference on Multimedia information retrieval10.1145/1743384.1743443(367-376)Online publication date: 29-Mar-2010
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media