{"id":58,"date":"2015-10-01T03:15:11","date_gmt":"2015-10-01T03:15:11","guid":{"rendered":"http:\/\/camp16.pecamp.org\/?page_id=58"},"modified":"2016-08-04T01:25:36","modified_gmt":"2016-08-04T01:25:36","slug":"keynotes","status":"publish","type":"page","link":"https:\/\/camp16.pecamp.org\/index.php\/keynotes\/","title":{"rendered":"Keynote Speaker"},"content":{"rendered":"<p><strong>Title:\u00a0Visual mining &#8211; interpreting image and video<\/strong><\/p>\n<p><strong>Abstract:<\/strong><\/p>\n<p>Like text mining, visual media mining tries to make sense of the<br clear=\"none\" \/>world through algorithms &#8211; albeit by analysing pixels instead of words.<br clear=\"none\" \/>This talk highlights recent important technical advances in automated<br clear=\"none\" \/>media understanding, which has a variety of applications ranging from<br clear=\"none\" \/>machines emulating the human aesthetic judgment of photographs to typical<br clear=\"none\" \/>visual mining tasks such as analysing food images. Highlighted techniques<br clear=\"none\" \/>include near-duplicate detection, multimedia indexing and the role of<br clear=\"none\" \/>machine learning. While the first two enable visual search engines so<br clear=\"none\" \/>that, eg, a snapshot of a smart-phone alone links the real world to<br clear=\"none\" \/>databases with information about it, machine learning ultimately is the<br clear=\"none\" \/>key to endowing machines with human capabilities of recognition and<br clear=\"none\" \/>interpretation. The talk will end by looking into the crystal ball<br clear=\"none\" \/>exploring what machines might learn from automatically analysing tens of<br clear=\"none\" \/>thousands of hours of TV footage.<\/p>\n<p><strong>Speaker:<\/strong><\/p>\n<table border=\"0\" width=\"70%\" cellspacing=\"2\" cellpadding=\"2\">\n<tbody>\n<tr>\n<td>\n<div align=\"center\">\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/camp16.pecamp.org\/wp-content\/uploads\/2016\/03\/stefan-rueger.png\" alt=\"\" width=\"148\" height=\"211\" align=\"middle\" \/><\/p>\n<h3><strong>Stefan R\u00fcger<\/strong><\/h3>\n<\/div>\n<\/td>\n<td><strong>Stefan R\u00fcger<\/strong> is a <strong>Professor of Knowledge Media<\/strong> at the <strong>Knowledge Media Institute<\/strong> of <strong>The Open University .<\/strong>\u00a0He read Physics at Freie Universit\u00e4t Berlin and<br clear=\"none\" \/>gained his PhD in Computer Science at Technische Universit\u00e4t Berlin (1996)<br clear=\"none\" \/>on the Theory of Neural Networks. During the next decade he carved out his<br clear=\"none\" \/>academic career from postdoc to Reader researching multimedia information<br clear=\"none\" \/>retrieval at Imperial College London, University of London, where he also<br clear=\"none\" \/>held an EPSRC Advanced Research Fellowship (1999-2004). In 2006 Stefan<br clear=\"none\" \/>became a Professor of Knowledge Media when he joined The Open University&#8217;s<br clear=\"none\" \/>Knowledge Media Institute to cover the area of Multimedia and Information<br clear=\"none\" \/>Systems. He currently holds an Honorary Professorship from the University<br clear=\"none\" \/>of Waikato, New Zealand, and has held Visiting Fellowships at Imperial<br clear=\"none\" \/>College London and Cranfield University, UK. Stefan is interested in the<br clear=\"none\" \/>intellectual challenge of visual processing with a view to automated<br clear=\"none\" \/>multimedia understanding<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>Title:\u00a0Visual mining &#8211; interpreting image and video Abstract: Like text mining, visual media mining tries to make sense of theworld through algorithms &#8211; albeit by analysing pixels instead of words.This talk highlights recent important technical advances in automatedmedia understanding, which has a variety of applications ranging frommachines emulating the human aesthetic judgment of photographs to &hellip; <a href=\"https:\/\/camp16.pecamp.org\/index.php\/keynotes\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Keynote Speaker<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-58","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/pages\/58","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/comments?post=58"}],"version-history":[{"count":5,"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/pages\/58\/revisions"}],"predecessor-version":[{"id":263,"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/pages\/58\/revisions\/263"}],"wp:attachment":[{"href":"https:\/\/camp16.pecamp.org\/index.php\/wp-json\/wp\/v2\/media?parent=58"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}