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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Stottinger, J. Uijlings, J.R.R. Pandey, A.K. Sebe, N. Giunchiglia, F. |
| Copyright Year | 2012 |
| Description | Author affiliation: University of Trento, Trento, Italy (Stottinger, J.; Uijlings, J.R.R.; Pandey, A.K.; Sebe, N.; Giunchiglia, F.) |
| Abstract | Since high-level events in images (e.g. “dinner”, “motorcycle stunt”, etc.) may not be directly correlated with their visual appearance, low-level visual features do not carry enough semantics to classify such events satisfactorily. This paper explores a fully compositional approach for event based image retrieval which is able to overcome this shortcoming. Furthermore, the approach is fully scalable in both adding new events and new primitives. Using the Pascal VOC 2007 dataset, our contributions are the following: (i) We apply the Faceted Analysis-Synthesis Theory (FAST) to build a hierarchy of 228 high-level events. (ii) We show that rule-based classifiers are better suited for compositional recognition of events than SVMs. In addition, rule-based classifiers provide semantically meaningful event descriptions which help bridging the semantic gap. (iii) We demonstrate that compositionality enables unseen event recognition: we can use rules learned from non-visual cues, together with object detectors to get reasonable performance on unseen event categories. |
| Starting Page | 3061 |
| Ending Page | 3068 |
| File Size | 244260 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467312264 |
| ISSN | 10636919 |
| e-ISBN | 9781467312288 |
| e-ISBN | 9781467312271 |
| DOI | 10.1109/CVPR.2012.6248037 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Motorcycles Semantics Visualization Detectors Training Support vector machines Humans |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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