{"id":1325,"date":"2014-05-27T08:42:18","date_gmt":"2014-05-27T07:42:18","guid":{"rendered":"http:\/\/www.ceessnoek.info\/?p=1325"},"modified":"2014-05-27T08:46:21","modified_gmt":"2014-05-27T07:46:21","slug":"best-practices-for-learning-video-concept-detectors-from-social-media-examples","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/best-practices-for-learning-video-concept-detectors-from-social-media-examples\/","title":{"rendered":"Best Practices for Learning Video Concept Detectors from Social Media Examples"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2014\/05\/best-practices-e1401176370472.png\" alt=\"best-practices\" width=\"350\" height=\"163\" align=\"right\" \/><\/p>\n<p>The paper &#8220;<em>Best Practices for Learning Video Concept Detectors from Social Media Examples<\/em>&#8221; by <a href=\"http:\/\/staff.science.uva.nl\/~skordumo\/\">Svetlana Kordumova<\/a>, Xirong Li, and Cees G. M. Snoek that will appear in a future special issue of Multimedia Tools and Applications is <a href=\"http:\/\/isis-data.science.uva.nl\/cgmsnoek\/pub\/kordumova-practices-mmta.pdf\">now available<\/a>. Learning video concept detectors from social media sources, such as Flickr images and YouTube videos, has the potential to address a wide variety of concept queries for video search. While the potential has been recognized by many, and progress on the topic has been impressive, we argue that key questions crucial to know how to learn effective video concept detectors from social media examples? remain open. As an initial attempt to answer these questions, we conduct an experimental study using a video search engine which is capable of learning concept detectors from social media examples, be it socially tagged videos or socially tagged images. Within the video search engine we investigate three strategies for positive example selection, three negative example selection strategies and three learning strategies. The performance is evaluated on the challenging TRECVID 2012 benchmark consisting of 600 h of Internet video. From the experiments we derive four best practices: (1) tagged images are a better source for learning video concepts than tagged videos, (2) selecting tag relevant positive training examples is always beneficial, (3) selecting relevant negative examples is advantageous and should be treated differently for video and image sources, and (4) learning concept detectors with selected relevant training data before learning is better then incorporating the relevance during the learning process. The best practices within our video search engine lead to state-of-the-art performance in the TRECVID 2013 benchmark for concept detection without manually provided annotations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper &#8220;Best Practices for Learning Video Concept Detectors from Social Media Examples&#8221; by Svetlana Kordumova, Xirong Li, and Cees G. M. Snoek that will appear in a future special issue of Multimedia Tools and Applications is now available. Learning video concept detectors from social media sources, such as Flickr images and YouTube videos, has [&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-1325","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>Best Practices for Learning Video Concept Detectors from Social Media Examples - 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\/best-practices-for-learning-video-concept-detectors-from-social-media-examples\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Best Practices for Learning Video Concept Detectors from Social Media Examples - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The paper &#8220;Best Practices for Learning Video Concept Detectors from Social Media Examples&#8221; by Svetlana Kordumova, Xirong Li, and Cees G. M. Snoek that will appear in a future special issue of Multimedia Tools and Applications is now available. Learning video concept detectors from social media sources, such as Flickr images and YouTube videos, has [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ceessnoek.info\/index.php\/best-practices-for-learning-video-concept-detectors-from-social-media-examples\/\" \/>\n<meta property=\"og:site_name\" content=\"Cees Snoek\" \/>\n<meta property=\"article:published_time\" content=\"2014-05-27T07:42:18+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2014-05-27T07:46:21+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2014\/05\/best-practices-e1401176370472.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\/best-practices-for-learning-video-concept-detectors-from-social-media-examples\/\",\"url\":\"https:\/\/www.ceessnoek.info\/index.php\/best-practices-for-learning-video-concept-detectors-from-social-media-examples\/\",\"name\":\"Best Practices for Learning Video Concept Detectors from Social Media Examples - 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