{"status":"public","department":[{"_id":"HeEd"}],"language":[{"iso":"eng"}],"author":[{"last_name":"Ion","id":"29F89302-F248-11E8-B48F-1D18A9856A87","full_name":"Ion, Adrian","first_name":"Adrian"},{"first_name":"Joao","full_name":"Carreira, Joao","last_name":"Carreira"},{"full_name":"Sminchisescu, Cristian","last_name":"Sminchisescu","first_name":"Cristian"}],"year":"2011","conference":{"name":"NIPS: Neural Information Processing Systems","start_date":"2011-12-12","location":"Granada, Spain","end_date":"2011-12-14"},"type":"conference","citation":{"short":"A. Ion, J. Carreira, C. Sminchisescu, in:, NIPS Proceedings, Neural Information Processing Systems Foundation, 2011, pp. 1827–1835.","apa":"Ion, A., Carreira, J., &#38; Sminchisescu, C. (2011). Probabilistic joint image segmentation and labeling. In <i>NIPS Proceedings</i> (Vol. 24, pp. 1827–1835). Granada, Spain: Neural Information Processing Systems Foundation.","chicago":"Ion, Adrian, Joao Carreira, and Cristian Sminchisescu. “Probabilistic Joint Image Segmentation and Labeling.” In <i>NIPS Proceedings</i>, 24:1827–35. Neural Information Processing Systems Foundation, 2011.","ista":"Ion A, Carreira J, Sminchisescu C. 2011. Probabilistic joint image segmentation and labeling. NIPS Proceedings. NIPS: Neural Information Processing Systems vol. 24, 1827–1835.","mla":"Ion, Adrian, et al. “Probabilistic Joint Image Segmentation and Labeling.” <i>NIPS Proceedings</i>, vol. 24, Neural Information Processing Systems Foundation, 2011, pp. 1827–35.","ieee":"A. Ion, J. Carreira, and C. Sminchisescu, “Probabilistic joint image segmentation and labeling,” in <i>NIPS Proceedings</i>, Granada, Spain, 2011, vol. 24, pp. 1827–1835.","ama":"Ion A, Carreira J, Sminchisescu C. Probabilistic joint image segmentation and labeling. In: <i>NIPS Proceedings</i>. Vol 24. Neural Information Processing Systems Foundation; 2011:1827-1835."},"_id":"3266","date_created":"2018-12-11T12:02:21Z","publist_id":"3381","intvolume":"        24","publication":"NIPS Proceedings","scopus_import":1,"date_updated":"2021-01-12T07:42:15Z","quality_controlled":"1","publisher":"Neural Information Processing Systems Foundation","volume":24,"oa_version":"None","day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","month":"12","title":"Probabilistic joint image segmentation and labeling","abstract":[{"lang":"eng","text":"We present a joint image segmentation and labeling model (JSL) which, given a bag of figure-ground segment hypotheses extracted at multiple image locations and scales, constructs a joint probability distribution over both the compatible image interpretations (tilings or image segmentations) composed from those segments, and over their labeling into categories. The process of drawing samples from the joint distribution can be interpreted as first sampling tilings, modeled as maximal cliques, from a graph connecting spatially non-overlapping segments in the bag [1], followed by sampling labels for those segments, conditioned on the choice of a particular tiling. We learn the segmentation and labeling parameters jointly, based on Maximum Likelihood with a novel Incremental Saddle Point estimation procedure. The partition function over tilings and labelings is increasingly more accurately approximated by including incorrect configurations that a not-yet-competent model rates probable during learning. We show that the proposed methodologymatches the current state of the art in the Stanford dataset [2], as well as in VOC2010, where 41.7% accuracy on the test set is achieved."}],"date_published":"2011-12-01T00:00:00Z","page":"1827 - 1835"}