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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lee, Y.J. Efros, A.A. Hebert, M. |
| Copyright Year | 2013 |
| Abstract | We present a weakly-supervised visual data mining approach that discovers connections between recurring mid-level visual elements in historic (temporal) and geographic (spatial) image collections, and attempts to capture the underlying visual style. In contrast to existing discovery methods that mine for patterns that remain visually consistent throughout the dataset, our goal is to discover visual elements whose appearance changes due to change in time or location; i.e., exhibit consistent stylistic variations across the label space (date or geo-location). To discover these elements, we first identify groups of patches that are style-sensitive. We then incrementally build correspondences to find the same element across the entire dataset. Finally, we train style-aware regressors that model each element's range of stylistic differences. We apply our approach to date and geo-location prediction and show substantial improvement over several baselines that do not model visual style. We also demonstrate the method's effectiveness on the related task of fine-grained classification. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 1857 |
| Ending Page | 1864 |
| File Size | 2996482 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479928408 |
| ISSN | 15505499 |
| DOI | 10.1109/ICCV.2013.233 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-01 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Visualization Detectors Training Databases Data mining Global Positioning System Histograms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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