%0 Conference Paper %1 Quattoni2005Incorporating %A Quattoni, Ariadna %A Collins, Michael %A Darrell, Trevor %B ICCVW '05: Proceedings of the Tenth IEEE International Conference on Computer Vision Workshops %C Washington, DC, USA %D 2005 %I IEEE Computer Society %K context image labeling phd region thesis-fuzzydls %P 1877 %R http://dx.doi.org/10.1109/ICCV.2005.256 %T Incorporating Semantic Constraints into a Discriminative Categorization and Labelling Model. %U http://portal.acm.org/citation.cfm?id=1153918.1154026&coll=GUIDE&dl=GUIDE %X This paper describes an approach to incorporate semantic knowledge sources within a discriminative learning framework. We consider a joint scene categorization and region labelling task and assume that some semantic knowledge is available. For example we might know what objects are allowed to appear in a given scene. Our goal is to use this knowledge to minimize the number of fully labelled examples (i.e. data for which each region in the image is labelled) required for learning. For each scene category the probability of a given labelling of image regions is modelled by a Conditional Random Field (CRF). Our model extends the CRF framework by incorporating hidden variables and combining class conditional CRFs into a joint framework for scene categorization and region labelling. We integrate semantic knowledge into the model by constraining the configurations that the latent region label variable can take, i.e. by constraining the possible region labelling for a given scene category. In a series of synthetic experiments, designed to illustrate the feasibility of the approach, adding semantic constraints about object entailment increased the region labelling accuracy given a fixed amount of fully labelled data. %@ 0-7695-2658-6

@inproceedings{Quattoni2005Incorporating, abstract = {This paper describes an approach to incorporate semantic knowledge sources within a discriminative learning framework. We consider a joint scene categorization and region labelling task and assume that some semantic knowledge is available. For example we might know what objects are allowed to appear in a given scene. Our goal is to use this knowledge to minimize the number of fully labelled examples (i.e. data for which each region in the image is labelled) required for learning. For each scene category the probability of a given labelling of image regions is modelled by a Conditional Random Field (CRF). Our model extends the CRF framework by incorporating hidden variables and combining class conditional CRFs into a joint framework for scene categorization and region labelling. We integrate semantic knowledge into the model by constraining the configurations that the latent region label variable can take, i.e. by constraining the possible region labelling for a given scene category. In a series of synthetic experiments, designed to illustrate the feasibility of the approach, adding semantic constraints about object entailment increased the region labelling accuracy given a fixed amount of fully labelled data.}, added-at = {2008-11-12T09:30:10.000+0100}, address = {Washington, DC, USA}, author = {Quattoni, Ariadna and Collins, Michael and Darrell, Trevor}, biburl = {https://www.bibsonomy.org/bibtex/27035cc6aed98055601c1f0c39ab64cbc/casi}, booktitle = {ICCVW '05: Proceedings of the Tenth IEEE International Conference on Computer Vision Workshops}, description = {Incorporating Semantic Constraints into a Discriminative Categorization and Labelling Model.}, doi = {http://dx.doi.org/10.1109/ICCV.2005.256}, interhash = {bd8ae92a019ecf79148155c2df7bd9ce}, intrahash = {7035cc6aed98055601c1f0c39ab64cbc}, isbn = {0-7695-2658-6}, keywords = {context image labeling phd region thesis-fuzzydls}, pages = 1877, publisher = {IEEE Computer Society}, timestamp = {2009-07-13T09:18:17.000+0200}, title = {Incorporating Semantic Constraints into a Discriminative Categorization and Labelling Model.}, url = {http://portal.acm.org/citation.cfm?id=1153918.1154026&coll=GUIDE&dl=GUIDE}, year = 2005 }