A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Ensembling neural networks: many could be better than all
Artificial Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
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
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Using the forest to see the trees: exploiting context for visual object detection and localization
Communications of the ACM
Scene Classification Using Spatial Pyramid of Latent Topics
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Maintaining internal consistency of report for real-time OLAP with layer-based view
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
Location and route tracking is important for visitors and travelers, and it mainly depends on GPS information. However, GPS devices are not usually carried with by the travelers. Mobile phone with digital camera is the common standing item for people. We try to analyze the photos from mobile and compared to the known scenic, then predict the user location and accomplish the route tracking according the time and spatial information. In this paper, we choose our university as the scenic, and get good performance on daytime.