The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Objects in Context
Abstract
In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model. Using a conditional random field (CRF) framework, our approach maximizes object label agreement according to contextual relevance. We compare two sources of context: one learned from training data and another queried from Google Sets. The overall performance of the proposed framework is evaluated on the PASCAL and MSRC datasets. Our findings conclude that incorporating context into object categorization greatly improves categorization accuracy.
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Citations
Cites
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Metadata
- Authors
- Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., & Belongie, S.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2081293863
- Cited by
- 594
- References
- 2
- FWCI
- 38.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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