{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:03:48Z","timestamp":1761581028782,"version":"3.41.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2011,2]]},"abstract":"<jats:p>Online social network services pose great opportunities and challenges for many research areas. In multimedia content analysis, automatic social group recommendation for images holds the promise to expand one's social network through media sharing. However, most existing techniques cannot generate satisfactory social group suggestions when the images are classified independently. In this article, we present novel methods to produce accurate suggestions of suitable social groups from a user's personal photo collection. First, an automatic clustering process is designed to estimate the group similarities, select the optimal number of clusters and categorize the social groups. Both visual content and textual annotations are integrated to generate initial predictions of the group categories for the images. Next, the relationship among images in a user's collection is modeled as a sparse graph. A collection-based sparse label propagation method is proposed to improve the group suggestions. Furthermore, the sparse graph-based collection model can be readily exploited to select the most influential and informative samples for active relevance feedback, which can be integrated with the label propagation process without the need for classifier retraining. The proposed methods have been tested on group suggestion tasks for real user collections and demonstrated superior performance over the state-of-the-art techniques.<\/jats:p>","DOI":"10.1145\/1899412.1899416","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Collection-based sparse label propagation and its application on social group suggestion from photos"],"prefix":"10.1145","volume":"2","author":[{"given":"Jie","family":"Yu","sequence":"first","affiliation":[{"name":"General Electric Company, Niskayuna, NY"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}]},{"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"Eastman Kodak Company, New York, NY"}]}],"member":"320","published-online":{"date-parts":[[2011,2,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1240624.1240772"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Blei D. Ng A. and Jordan M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. Blei D. Ng A. and Jordan M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res.","DOI":"10.7551\/mitpress\/1120.003.0082"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459376"},{"key":"e_1_2_1_5_1","volume-title":"CEDD: Color and Edge Directivity Descriptor: A compact descriptor for image indexing and retrieval. Comput. Vis. Syst., 312--322.","author":"Chatzichristofis S.","year":"2008","unstructured":"Chatzichristofis , S. and Boutalis , Y . 2008 . CEDD: Color and Edge Directivity Descriptor: A compact descriptor for image indexing and retrieval. Comput. Vis. Syst., 312--322. Chatzichristofis, S. and Boutalis, Y. 2008. CEDD: Color and Edge Directivity Descriptor: A compact descriptor for image indexing and retrieval. Comput. Vis. Syst., 312--322."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459473"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526801"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646445"},{"key":"e_1_2_1_9_1","volume-title":"et al","author":"Efron B.","year":"2003","unstructured":"Efron , B. et al . 2003 . Least angle regression. Ann. Stat . Efron, B. et al. 2003. Least angle regression. Ann. Stat."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081877"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.170"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Griffiths T. and Steyvers M. 2004. Finding scientific topics. Proc. Nat. Acad. Sci. USA. Griffiths T. and Steyvers M. 2004. Finding scientific topics. Proc. Nat. Acad. Sci. USA.","DOI":"10.1073\/pnas.0307752101"},{"volume-title":"The Elements of Statistical Learning: Data Mining, Inference and Prediction","author":"Hastie T.","key":"e_1_2_1_13_1","unstructured":"Hastie , T. , Tibshirani , R. , and Friedman , J . The Elements of Statistical Learning: Data Mining, Inference and Prediction . Springer , New York . Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York."},{"volume-title":"Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.","author":"Hays J.","key":"e_1_2_1_14_1","unstructured":"Hays , J. and Efros , A . 2008. IM2GPS: Estimating geographic information from a single image . In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Hays, J. and Efros, A. 2008. IM2GPS: Estimating geographic information from a single image. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312649"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775126"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459421"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081945"},{"volume-title":"Proceedings of the Internationals Conference on WWW.","author":"Jing Y.","key":"e_1_2_1_20_1","unstructured":"Jing , Y. and Baluja , S . 2008. VisualRank: Applying PageRank to large-scale image search . In Proceedings of the Internationals Conference on WWW. Jing, Y. and Baluja, S. 2008. VisualRank: Applying PageRank to large-scale image search. In Proceedings of the Internationals Conference on WWW."},{"volume-title":"Proceedings of the International Conference on Computer Vision and Pattern Recognition.","author":"Joshi A.","key":"e_1_2_1_21_1","unstructured":"Joshi , A. , Porikli , F. , and Papanikolopoulos , N . 2008. Multi-class active learning for image classification . In Proceedings of the International Conference on Computer Vision and Pattern Recognition. Joshi, A., Porikli, F., and Papanikolopoulos, N. 2008. Multi-class active learning for image classification. In Proceedings of the International Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1386352.1386361"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460126"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459574"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1386352.1386406"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011139631724"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526813"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Rao R. Olshausen B. and Lewicki M. 2002. Probabilistic Models of Brain: Perception and Neural Function. MIT Press. Rao R. Olshausen B. and Lewicki M. 2002. Probabilistic Models of Brain: Perception and Neural Function. MIT Press.","DOI":"10.7551\/mitpress\/5583.001.0001"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Roweis S. and Saul L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science. Roweis S. and Saul L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science.","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/76.718510"},{"volume-title":"Proceedings of the 1st IEEE Workshop on Internet Vision at CVPR 08","author":"Sorokin A.","key":"e_1_2_1_31_1","unstructured":"Sorokin , A. and Forsyth , D . 2008. Utility data annotation with Amazon Mechanical Turk . In Proceedings of the 1st IEEE Workshop on Internet Vision at CVPR 08 . Sorokin, A. and Forsyth, D. 2008. Utility data annotation with Amazon Mechanical Turk. In Proceedings of the 1st IEEE Workshop on Internet Vision at CVPR 08."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.921853"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_2_1_34_1","unstructured":"Tong W. and Jin R. 2007. Semi-Supervised Learning by Mixed Label Propagation. AAAI. Tong W. and Jin R. 2007. Semi-Supervised Learning by Mixed Label Propagation. AAAI."},{"key":"e_1_2_1_35_1","unstructured":"Torralba A. Fergus R. and Freeman W. 2007. Tiny images. Tech. rep. MIT-CSAIL-TR-2007-024. Torralba A. Fergus R. and Freeman W. 2007. Tiny images. Tech. rep. MIT-CSAIL-TR-2007-024."},{"key":"e_1_2_1_36_1","unstructured":"Verma D. and Meila M. 2003. A comparison of spectral clustering algorithms. Tech. rep. University of Washington. Verma D. and Meila M. 2003. A comparison of spectral clustering algorithms. Tech. rep. University of Washington."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390396"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190672"},{"volume-title":"Proceedings of the International Conference on Computer Vision.","author":"Wang G.","key":"e_1_2_1_39_1","unstructured":"Wang , G. , Hoiem , D. , and Forsyth , D . 2009. Learning image similarity from Flickr groups using stochastic intersection kernel machines . In Proceedings of the International Conference on Computer Vision. Wang, G., Hoiem, D., and Forsyth, D. 2009. Learning image similarity from Flickr groups using stochastic intersection kernel machines. In Proceedings of the International Conference on Computer Vision."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459375"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1631272.1631293"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526758"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390375"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70714"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/1386352.1386379"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/276304.276388"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1899412.1899416","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1899412.1899416","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T10:59:46Z","timestamp":1750244386000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1899412.1899416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,2]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2011,2]]}},"alternative-id":["10.1145\/1899412.1899416"],"URL":"https:\/\/doi.org\/10.1145\/1899412.1899416","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2011,2]]},"assertion":[{"value":"2010-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2010-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2011-02-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}