[{"page":"11 - 20","abstract":[{"text":"We study the task of interactive semantic labeling of a segmentation hierarchy. To this end we propose a framework interleaving two components: an automatic labeling step, based on a Conditional Random Field whose dependencies are defined by the inclusion tree of the segmentation hierarchy, and an interaction step that integrates incremental input from a human user. Evaluated on two distinct datasets, the proposed interactive approach efficiently integrates human interventions and illustrates the advantages of structured prediction in an interactive framework. ","lang":"eng"}],"_id":"2971","department":[{"_id":"HeEd"}],"month":"01","date_published":"2012-01-01T00:00:00Z","year":"2012","date_updated":"2021-01-12T07:40:10Z","day":"01","scopus_import":1,"doi":"10.1007/978-3-642-32717-9_2","language":[{"iso":"eng"}],"intvolume":" 7476","publist_id":"3737","date_created":"2018-12-11T12:00:37Z","author":[{"full_name":"Zankl, Georg","first_name":"Georg","last_name":"Zankl"},{"last_name":"Haxhimusa","first_name":"Yll","full_name":"Haxhimusa, Yll"},{"first_name":"Adrian","id":"29F89302-F248-11E8-B48F-1D18A9856A87","last_name":"Ion","full_name":"Ion, Adrian"}],"citation":{"ieee":"G. Zankl, Y. Haxhimusa, and A. Ion, “Interactive labeling of image segmentation hierarchies,” presented at the Pattern Recognition, Graz, Austria, 2012, vol. 7476, pp. 11–20.","short":"G. Zankl, Y. Haxhimusa, A. Ion, in:, Springer, 2012, pp. 11–20.","apa":"Zankl, G., Haxhimusa, Y., & Ion, A. (2012). Interactive labeling of image segmentation hierarchies (Vol. 7476, pp. 11–20). Presented at the Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_2","ama":"Zankl G, Haxhimusa Y, Ion A. Interactive labeling of image segmentation hierarchies. In: Vol 7476. Springer; 2012:11-20. doi:10.1007/978-3-642-32717-9_2","ista":"Zankl G, Haxhimusa Y, Ion A. 2012. Interactive labeling of image segmentation hierarchies. Pattern Recognition vol. 7476, 11–20.","chicago":"Zankl, Georg, Yll Haxhimusa, and Adrian Ion. “Interactive Labeling of Image Segmentation Hierarchies,” 7476:11–20. Springer, 2012. https://doi.org/10.1007/978-3-642-32717-9_2.","mla":"Zankl, Georg, et al. Interactive Labeling of Image Segmentation Hierarchies. Vol. 7476, Springer, 2012, pp. 11–20, doi:10.1007/978-3-642-32717-9_2."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","publisher":"Springer","title":"Interactive labeling of image segmentation hierarchies","volume":7476,"type":"conference","quality_controlled":"1","oa_version":"None","conference":{"name":"Pattern Recognition","location":"Graz, Austria","start_date":"2012-08-28","end_date":"2012-08-31"},"publication_status":"published"},{"citation":{"ama":"Ion A, Carreira J, Sminchisescu C. Image segmentation by figure-ground composition into maximal cliques. In: IEEE; 2012. doi:10.1109/ICCV.2011.6126486","ista":"Ion A, Carreira J, Sminchisescu C. 2012. Image segmentation by figure-ground composition into maximal cliques. ICCV: International Conference on Computer Vision, 6126486.","mla":"Ion, Adrian, et al. Image Segmentation by Figure-Ground Composition into Maximal Cliques. 6126486, IEEE, 2012, doi:10.1109/ICCV.2011.6126486.","chicago":"Ion, Adrian, Joao Carreira, and Cristian Sminchisescu. “Image Segmentation by Figure-Ground Composition into Maximal Cliques.” IEEE, 2012. https://doi.org/10.1109/ICCV.2011.6126486.","ieee":"A. Ion, J. Carreira, and C. Sminchisescu, “Image segmentation by figure-ground composition into maximal cliques,” presented at the ICCV: International Conference on Computer Vision, Barcelona, Spain, 2012.","apa":"Ion, A., Carreira, J., & Sminchisescu, C. (2012). Image segmentation by figure-ground composition into maximal cliques. Presented at the ICCV: International Conference on Computer Vision, Barcelona, Spain: IEEE. https://doi.org/10.1109/ICCV.2011.6126486","short":"A. Ion, J. Carreira, C. Sminchisescu, in:, IEEE, 2012."},"publication_status":"published","conference":{"start_date":"2011-11-06","location":"Barcelona, Spain","name":"ICCV: International Conference on Computer Vision","end_date":"2011-11-13"},"oa_version":"None","quality_controlled":"1","publisher":"IEEE","status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","type":"conference","title":"Image segmentation by figure-ground composition into maximal cliques","language":[{"iso":"eng"}],"doi":"10.1109/ICCV.2011.6126486","author":[{"full_name":"Ion, Adrian","last_name":"Ion","id":"29F89302-F248-11E8-B48F-1D18A9856A87","first_name":"Adrian"},{"full_name":"Carreira, Joao","first_name":"Joao","last_name":"Carreira"},{"last_name":"Sminchisescu","first_name":"Cristian","full_name":"Sminchisescu, Cristian"}],"publist_id":"3382","date_created":"2018-12-11T12:02:21Z","date_updated":"2021-01-12T07:42:15Z","year":"2012","day":"12","_id":"3265","department":[{"_id":"HeEd"}],"abstract":[{"lang":"eng","text":"We propose a mid-level statistical model for image segmentation that composes multiple figure-ground hypotheses (FG) obtained by applying constraints at different locations and scales, into larger interpretations (tilings) of the entire image. Inference is cast as optimization over sets of maximal cliques sampled from a graph connecting all non-overlapping figure-ground segment hypotheses. Potential functions over cliques combine unary, Gestalt-based figure qualities, and pairwise compatibilities among spatially neighboring segments, constrained by T-junctions and the boundary interface statistics of real scenes. Learning the model parameters is based on maximum likelihood, alternating between sampling image tilings and optimizing their potential function parameters. State of the art results are reported on the Berkeley and Stanford segmentation datasets, as well as VOC2009, where a 28% improvement was achieved."}],"article_number":"6126486","month":"01","date_published":"2012-01-12T00:00:00Z"},{"date_updated":"2021-01-12T07:42:15Z","year":"2011","scopus_import":1,"day":"01","_id":"3266","department":[{"_id":"HeEd"}],"page":"1827 - 1835","abstract":[{"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.","lang":"eng"}],"month":"12","date_published":"2011-12-01T00:00:00Z","citation":{"ieee":"A. Ion, J. Carreira, and C. Sminchisescu, “Probabilistic joint image segmentation and labeling,” in NIPS Proceedings, Granada, Spain, 2011, vol. 24, pp. 1827–1835.","short":"A. Ion, J. Carreira, C. Sminchisescu, in:, NIPS Proceedings, Neural Information Processing Systems Foundation, 2011, pp. 1827–1835.","apa":"Ion, A., Carreira, J., & Sminchisescu, C. (2011). Probabilistic joint image segmentation and labeling. In NIPS Proceedings (Vol. 24, pp. 1827–1835). Granada, Spain: Neural Information Processing Systems Foundation.","ama":"Ion A, Carreira J, Sminchisescu C. Probabilistic joint image segmentation and labeling. In: NIPS Proceedings. Vol 24. Neural Information Processing Systems Foundation; 2011:1827-1835.","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.","chicago":"Ion, Adrian, Joao Carreira, and Cristian Sminchisescu. “Probabilistic Joint Image Segmentation and Labeling.” In NIPS Proceedings, 24:1827–35. Neural Information Processing Systems Foundation, 2011.","mla":"Ion, Adrian, et al. “Probabilistic Joint Image Segmentation and Labeling.” NIPS Proceedings, vol. 24, Neural Information Processing Systems Foundation, 2011, pp. 1827–35."},"conference":{"end_date":"2011-12-14","name":"NIPS: Neural Information Processing Systems","start_date":"2011-12-12","location":"Granada, Spain"},"oa_version":"None","publication_status":"published","quality_controlled":"1","publisher":"Neural Information Processing Systems Foundation","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","volume":24,"title":"Probabilistic joint image segmentation and labeling","type":"conference","language":[{"iso":"eng"}],"intvolume":" 24","publication":"NIPS Proceedings","author":[{"full_name":"Ion, Adrian","first_name":"Adrian","id":"29F89302-F248-11E8-B48F-1D18A9856A87","last_name":"Ion"},{"full_name":"Carreira, Joao","first_name":"Joao","last_name":"Carreira"},{"first_name":"Cristian","last_name":"Sminchisescu","full_name":"Sminchisescu, Cristian"}],"publist_id":"3381","date_created":"2018-12-11T12:02:21Z"},{"article_processing_charge":"No","page":"286-293","abstract":[{"text":"In this paper, we establish a correspondence between the incremental algorithm for computing AT-models [8,9] and the one for computing persistent homology [6,14,15]. We also present a decremental algorithm for computing AT-models that allows to extend the persistence computation to a wider setting. Finally, we show how to combine incremental and decremental techniques for persistent homology computation.","lang":"eng"}],"scopus_import":"1","date_created":"2021-07-11T22:01:19Z","oa":1,"oa_version":"Published Version","quality_controlled":"1","title":"Incremental-decremental algorithm for computing AT-models and persistent homology","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","publisher":"Springer Nature","main_file_link":[{"open_access":"1","url":"http://hdl.handle.net/11441/30766"}],"alternative_title":["LNCS"],"date_published":"2011-08-01T00:00:00Z","month":"08","department":[{"_id":"HeEd"}],"_id":"9648","day":"01","date_updated":"2021-08-12T13:53:17Z","year":"2011","author":[{"full_name":"Gonzalez-Diaz, Rocio","first_name":"Rocio","last_name":"Gonzalez-Diaz"},{"full_name":"Ion, Adrian","first_name":"Adrian","id":"29F89302-F248-11E8-B48F-1D18A9856A87","last_name":"Ion"},{"full_name":"Jimenez, Maria Jose","first_name":"Maria Jose","last_name":"Jimenez"},{"first_name":"Regina","last_name":"Poyatos","full_name":"Poyatos, Regina"}],"language":[{"iso":"eng"}],"intvolume":" 6854","publication_identifier":{"issn":["03029743"],"eissn":["16113349"],"isbn":["9783642236716"]},"publication":"Computer Analysis of Images and Patterns","doi":"10.1007/978-3-642-23672-3_35","publication_status":"published","conference":{"name":"CAIP: International Conference on Computer Analysis of Images and Patterns","start_date":"2011-08-29","location":"Seville, Spain","end_date":"2011-08-31"},"volume":6854,"type":"conference","status":"public","citation":{"ieee":"R. Gonzalez-Diaz, A. Ion, M. J. Jimenez, and R. Poyatos, “Incremental-decremental algorithm for computing AT-models and persistent homology,” in Computer Analysis of Images and Patterns, Seville, Spain, 2011, vol. 6854, pp. 286–293.","apa":"Gonzalez-Diaz, R., Ion, A., Jimenez, M. J., & Poyatos, R. (2011). Incremental-decremental algorithm for computing AT-models and persistent homology. In Computer Analysis of Images and Patterns (Vol. 6854, pp. 286–293). Seville, Spain: Springer Nature. https://doi.org/10.1007/978-3-642-23672-3_35","short":"R. Gonzalez-Diaz, A. Ion, M.J. Jimenez, R. Poyatos, in:, Computer Analysis of Images and Patterns, Springer Nature, 2011, pp. 286–293.","ista":"Gonzalez-Diaz R, Ion A, Jimenez MJ, Poyatos R. 2011. Incremental-decremental algorithm for computing AT-models and persistent homology. Computer Analysis of Images and Patterns. CAIP: International Conference on Computer Analysis of Images and Patterns, LNCS, vol. 6854, 286–293.","ama":"Gonzalez-Diaz R, Ion A, Jimenez MJ, Poyatos R. Incremental-decremental algorithm for computing AT-models and persistent homology. In: Computer Analysis of Images and Patterns. Vol 6854. Springer Nature; 2011:286-293. doi:10.1007/978-3-642-23672-3_35","mla":"Gonzalez-Diaz, Rocio, et al. “Incremental-Decremental Algorithm for Computing AT-Models and Persistent Homology.” Computer Analysis of Images and Patterns, vol. 6854, Springer Nature, 2011, pp. 286–93, doi:10.1007/978-3-642-23672-3_35.","chicago":"Gonzalez-Diaz, Rocio, Adrian Ion, Maria Jose Jimenez, and Regina Poyatos. “Incremental-Decremental Algorithm for Computing AT-Models and Persistent Homology.” In Computer Analysis of Images and Patterns, 6854:286–93. Springer Nature, 2011. https://doi.org/10.1007/978-3-642-23672-3_35."}},{"scopus_import":"1","page":"215-224","abstract":[{"text":"This paper presents a method to create a model of an articulated object using the planar motion in an initialization video. The model consists of rigid parts connected by points of articulation. The rigid parts are described by the positions of salient feature-points tracked throughout the video. Following a filtering step that identifies points that belong to different objects, rigid parts are found by a grouping process in a graph pyramid. Valid articulation points are selected by verifying multiple hypotheses for each pair of parts.","lang":"eng"}],"article_processing_charge":"No","title":"Spatio-temporal extraction of articulated models in a graph pyramid","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publisher":"Springer","oa_version":"None","quality_controlled":"1","date_created":"2022-03-21T08:08:35Z","year":"2011","date_updated":"2023-09-05T14:10:15Z","day":"01","department":[{"_id":"HeEd"}],"_id":"10907","date_published":"2011-06-01T00:00:00Z","month":"06","alternative_title":["LNCS"],"place":"Berlin, Heidelberg","citation":{"mla":"Artner, Nicole M., et al. “Spatio-Temporal Extraction of Articulated Models in a Graph Pyramid.” Graph-Based Representations in Pattern Recognition, edited by Xiaoyi Jiang et al., vol. 6658, Springer, 2011, pp. 215–24, doi:10.1007/978-3-642-20844-7_22.","chicago":"Artner, Nicole M., Adrian Ion, and Walter G. Kropatsch. “Spatio-Temporal Extraction of Articulated Models in a Graph Pyramid.” In Graph-Based Representations in Pattern Recognition, edited by Xiaoyi Jiang, Miquel Ferrer, and Andrea Torsello, 6658:215–24. LNIP. Berlin, Heidelberg: Springer, 2011. https://doi.org/10.1007/978-3-642-20844-7_22.","ama":"Artner NM, Ion A, Kropatsch WG. Spatio-temporal extraction of articulated models in a graph pyramid. In: Jiang X, Ferrer M, Torsello A, eds. Graph-Based Representations in Pattern Recognition. Vol 6658. LNIP. Berlin, Heidelberg: Springer; 2011:215-224. doi:10.1007/978-3-642-20844-7_22","ista":"Artner NM, Ion A, Kropatsch WG. 2011. Spatio-temporal extraction of articulated models in a graph pyramid. Graph-Based Representations in Pattern Recognition. GbRPR: Graph-based Representations in Pattern RecognitionLNIP, LNCS, vol. 6658, 215–224.","short":"N.M. Artner, A. Ion, W.G. Kropatsch, in:, X. Jiang, M. Ferrer, A. Torsello (Eds.), Graph-Based Representations in Pattern Recognition, Springer, Berlin, Heidelberg, 2011, pp. 215–224.","apa":"Artner, N. M., Ion, A., & Kropatsch, W. G. (2011). Spatio-temporal extraction of articulated models in a graph pyramid. In X. Jiang, M. Ferrer, & A. Torsello (Eds.), Graph-Based Representations in Pattern Recognition (Vol. 6658, pp. 215–224). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-20844-7_22","ieee":"N. M. Artner, A. Ion, and W. G. Kropatsch, “Spatio-temporal extraction of articulated models in a graph pyramid,” in Graph-Based Representations in Pattern Recognition, Münster, Germany, 2011, vol. 6658, pp. 215–224."},"volume":6658,"type":"conference","editor":[{"first_name":"Xiaoyi","last_name":"Jiang","full_name":"Jiang, Xiaoyi"},{"first_name":"Miquel","last_name":"Ferrer","full_name":"Ferrer, Miquel"},{"first_name":"Andrea","last_name":"Torsello","full_name":"Torsello, Andrea"}],"status":"public","conference":{"end_date":"2011-05-20","name":"GbRPR: Graph-based Representations in Pattern Recognition","location":"Münster, Germany","start_date":"2011-05-18"},"publication_status":"published","series_title":"LNIP","publication":"Graph-Based Representations in Pattern Recognition","doi":"10.1007/978-3-642-20844-7_22","acknowledgement":"This work has been partially supported by the Austrian Science Fund under grants S9103-N13 and P18716-N13.","intvolume":" 6658","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1611-3349"],"eisbn":["9783642208447"],"isbn":["9783642208430"],"issn":["0302-9743"]},"author":[{"last_name":"Artner","first_name":"Nicole M.","full_name":"Artner, Nicole M."},{"last_name":"Ion","first_name":"Adrian","id":"29F89302-F248-11E8-B48F-1D18A9856A87","full_name":"Ion, Adrian"},{"last_name":"Kropatsch","first_name":"Walter G.","full_name":"Kropatsch, Walter G."}]}]