Scenes vs. objects: A comparative study of two approaches to context based recognition
Research output: Contribution to journal › Conference article › Research › peer-review
Contextual models play a very important role in the task of object recognition. Over the years, two kinds of contextual models have emerged: models with contextual inference based on the statistical summary of the scene (we will refer to these as Scene Based Context models, or SBC), and models representing the context in terms of relationships among objects in the image (Object Based Context, or OBC). In designing object recognition systems, it is necessary to understand the theoretical and practical properties of such approaches. This work provides an analysis of these models and evaluates two of their representatives using the LabelMe dataset. We demonstrate a considerable margin of improvement using the OBC style approach.
Original language | English |
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Journal | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 |
Pages (from-to) | 92-99 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2009 |
Externally published | Yes |
Event | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 - Miami, FL, United States Duration: 20 Jun 2009 → 25 Jun 2009 |
Conference
Conference | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 |
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Country | United States |
City | Miami, FL |
Period | 20/06/2009 → 25/06/2009 |
ID: 302050349