Objects in context
Research output: Contribution to conference › Paper › Research › peer-review
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, oar 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 imrproves categorization accuracy.
Original language | English |
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Publication date | 2007 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Event | 2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil Duration: 14 Oct 2007 → 21 Oct 2007 |
Conference
Conference | 2007 IEEE 11th International Conference on Computer Vision, ICCV |
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Country | Brazil |
City | Rio de Janeiro |
Period | 14/10/2007 → 21/10/2007 |
ID: 302052099