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Graph."],"published-print":{"date-parts":[[2014,7,27]]},"abstract":"<jats:p>\n            We introduce\n            <jats:italic>focal points<\/jats:italic>\n            for characterizing, comparing, and organizing collections of complex and heterogeneous data and apply the concepts and algorithms developed to collections of 3D indoor scenes. We represent each scene by a graph of its constituent objects and define focal points as\n            <jats:italic>representative<\/jats:italic>\n            substructures in a scene collection. To organize a heterogeneous scene collection, we cluster the scenes based on a set of extracted focal points: scenes in a cluster are closely connected when viewed from the perspective of the representative focal points of that cluster. The key concept of representativity requires that the focal points occur\n            <jats:italic>frequently<\/jats:italic>\n            in the cluster and that they result in a\n            <jats:italic>compact<\/jats:italic>\n            cluster. Hence, the problem of focal point extraction is intermixed with the problem of clustering groups of scenes based on their representative focal points. We present a\n            <jats:italic>co-analysis<\/jats:italic>\n            algorithm which interleaves frequent pattern mining and subspace clustering to extract a set of\n            <jats:italic>contextual<\/jats:italic>\n            focal points which guide the clustering of the scene collection. We demonstrate advantages of\n            <jats:italic>focal-centric<\/jats:italic>\n            scene comparison and organization over existing approaches, particularly in dealing with\n            <jats:italic>hybrid<\/jats:italic>\n            scenes, scenes consisting of elements which suggest membership in different semantic categories.\n          <\/jats:p>","DOI":"10.1145\/2601097.2601109","type":"journal-article","created":{"date-parts":[[2014,7,22]],"date-time":"2014-07-22T15:08:20Z","timestamp":1406041700000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Organizing heterogeneous scene collections through contextual focal points"],"prefix":"10.1145","volume":"33","author":[{"given":"Kai","family":"Xu","sequence":"first","affiliation":[{"name":"Shenzhen VisuCA Key Lab\/SIAT and HPCL, National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Ma","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Zhu","sequence":"additional","affiliation":[{"name":"HPCL, National University of Defense Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel","family":"Shamir","sequence":"additional","affiliation":[{"name":"The Interdisciplinary Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab\/SIAT"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,7,27]]},"reference":[{"key":"e_1_2_2_1_1","first-page":"49","article-title":"A context similarity measure","volume":"784","author":"Biberman Y.","year":"1994","unstructured":"Biberman , Y. 1994 . A context similarity measure . Machine Learning 784 , 49 -- 63 . Biberman, Y. 1994. A context similarity measure. 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