Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Context data in geo-referenced digital photo collections
Proceedings of the 12th annual ACM international conference on Multimedia
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
| Hi-index | 0.00 |
Visual summarization of landmarks is an interesting and non-trivial task with the availability of gigantic community-contributed resources. In this work, we investigate ways to generate representative and distinctive views of landmarks by automatically discovering the underlying Scenic Themes (e.g. sunny, night view, snow, foggy views, etc.) via a content-based analysis. The challenge is that the task suffers from the subjectivity of the scenic theme understanding, and there is lack of prior knowledge of scenic themes understanding. In addition, the visual variations of scenic themes are results of joint effects of factors including weather, time, season, etc. To tackle the aforementioned issues, we exploit the Dirichlet Process Gaussian Mixture Model (DPGMM). The major advantages in using DPGMM is that it is fully unsupervised and do not require the number of components to be fixed beforehand, which avoids the difficulty in adjusting model complexity to avoid over-fitting. This work makes the first attempt towards generation of representative views of landmarks via scenic theme mining. Testing on seven famous world landmarks show promising results.