Scenes vs. objects: A comparative study of two approaches to context based recognition
Research output: Contribution to journal › Conference article › Research › peer-review
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Scenes vs. objects : A comparative study of two approaches to context based recognition. / Belongie, Serge; Rabinovich, Andrew.
In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, p. 92-99.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Scenes vs. objects
T2 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
AU - Belongie, Serge
AU - Rabinovich, Andrew
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70449586747&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2009.5204220
DO - 10.1109/CVPR.2009.5204220
M3 - Conference article
AN - SCOPUS:70449586747
SP - 92
EP - 99
JO - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
JF - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Y2 - 20 June 2009 through 25 June 2009
ER -
ID: 302050349