{"id":1388,"date":"2014-10-11T13:31:21","date_gmt":"2014-10-11T12:31:21","guid":{"rendered":"http:\/\/www.ceessnoek.info\/?p=1388"},"modified":"2015-01-05T16:24:43","modified_gmt":"2015-01-05T15:24:43","slug":"conceptlets-selective-semantics-for-classifying-video-events","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/conceptlets-selective-semantics-for-classifying-video-events\/","title":{"rendered":"Conceptlets: Selective Semantics for Classifying Video Events"},"content":{"rendered":"<p><img decoding=\"async\" src=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2014\/10\/conceptlet-300x136.png\" alt=\"conceptlet\" width=\"400\" align=\"right\" \/><\/p>\n<p>The IEEE Transactions on Multimedia paper:\u00a0<em>Conceptlets: Selective Semantics for Classifying Video Events<\/em> by Masoud Mazloom, Efstrastios Gavves, and Cees Snoek is <a href=\"http:\/\/isis-data.science.uva.nl\/cgmsnoek\/pub\/mazloom-conceptlets-tmm.pdf\">now available<\/a>. An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are most informative per event, which we call the conceptlet. We model finding the conceptlet out of a large set of concept detectors as an importance sampling problem. Our proposed approximate algorithm finds the optimal conceptlet using a cross-entropy optimization. We study the behavior of video event classification based on conceptlets by performing four experiments on challenging internet video from the 2010 and 2012 TRECVID multimedia event detection tasks and Columbia\u2019s consumer video dataset. Starting from a concept bank of more than thousand precomputed detectors, our experiments establish there are (sets of) individual concept detectors that are more discriminative and appear to be more descriptive for a particular event than others, event classification using an automatically obtained conceptlet is more robust than using all available concepts, and conceplets obtained with our cross-entropy algorithm are better than conceptlets from state-of-the-art feature selection algorithms. What is more, the conceptlets make sense for the events of interest, without being programmed to do so.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The IEEE Transactions on Multimedia paper:\u00a0Conceptlets: Selective Semantics for Classifying Video Events by Masoud Mazloom, Efstrastios Gavves, and Cees Snoek is now available. An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-1388","post","type-post","status-publish","format-standard","hentry","category-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Conceptlets: Selective Semantics for Classifying Video Events - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/conceptlets-selective-semantics-for-classifying-video-events\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Conceptlets: Selective Semantics for Classifying Video Events - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The IEEE Transactions on Multimedia paper:\u00a0Conceptlets: Selective Semantics for Classifying Video Events by Masoud Mazloom, Efstrastios Gavves, and Cees Snoek is now available. An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ceessnoek.info\/index.php\/conceptlets-selective-semantics-for-classifying-video-events\/\" \/>\n<meta property=\"og:site_name\" content=\"Cees Snoek\" \/>\n<meta property=\"article:published_time\" content=\"2014-10-11T12:31:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2015-01-05T15:24:43+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2014\/10\/conceptlet-300x136.png\" \/>\n<meta name=\"author\" content=\"Cees\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cees\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.ceessnoek.info\/index.php\/conceptlets-selective-semantics-for-classifying-video-events\/\",\"url\":\"https:\/\/www.ceessnoek.info\/index.php\/conceptlets-selective-semantics-for-classifying-video-events\/\",\"name\":\"Conceptlets: Selective Semantics for Classifying Video Events - 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