{"id":845,"date":"2012-04-25T21:32:24","date_gmt":"2012-04-25T20:32:24","guid":{"rendered":"http:\/\/www.ceessnoek.info\/?p=845"},"modified":"2012-04-25T21:32:24","modified_gmt":"2012-04-25T20:32:24","slug":"convex-reduction-of-high-dimensional-kernels-for-visual-classification","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/convex-reduction-of-high-dimensional-kernels-for-visual-classification\/","title":{"rendered":"Convex Reduction of High-Dimensional Kernels for Visual Classification"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2012\/04\/kerneldimensions-300x200.jpg\" alt=\"\" width=\"300\" height=\"200\" align=\"right\" \/><\/p>\n<p>The forthcoming CVPR paper on Convex Reduction of High-Dimensional Kernels for Visual Classification by <a title=\"Stratis\" href=\"http:\/\/staff.science.uva.nl\/~gavves\/\">Efstratios Gavves<\/a>, Cees Snoek and Arnold Smeulders is <a href=\"http:\/\/staff.science.uva.nl\/~cgmsnoek\/pub\/gavves-convex-kernel-cvpr2012.pdf\">now available<\/a>. Limiting factors of fast and effective classifiers for large sets of images are their dependence on the number of images analyzed <em>and<\/em> the dimensionality of the image representation. Considering the growing number of images as a given, we aim to reduce the image feature dimensionality in this paper. We propose reduced linear kernels that use only a portion of the dimensions to reconstruct a linear kernel. We formulate the search for these dimensions as a convex optimization problem, which can be solved efficiently. Different from existing kernel reduction methods, our reduced kernels are faster and maintain the accuracy benefits from non-linear embedding methods that mimic non-linear SVMs. We show these properties on both the Scenes and PASCAL VOC 2007 datasets. In addition, we demonstrate how our reduced kernels allow to compress Fisher vector for use with non-linear embeddings, leading to high accuracy. What is more, without using any labeled examples the selected and weighed kernel dimensions appear to correspond to visually meaningful patches in the images.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The forthcoming CVPR paper on Convex Reduction of High-Dimensional Kernels for Visual Classification by Efstratios Gavves, Cees Snoek and Arnold Smeulders is now available. Limiting factors of fast and effective classifiers for large sets of images are their dependence on the number of images analyzed and the dimensionality of the image representation. Considering the growing [&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-845","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>Convex Reduction of High-Dimensional Kernels for Visual Classification - 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\/convex-reduction-of-high-dimensional-kernels-for-visual-classification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Convex Reduction of High-Dimensional Kernels for Visual Classification - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The forthcoming CVPR paper on Convex Reduction of High-Dimensional Kernels for Visual Classification by Efstratios Gavves, Cees Snoek and Arnold Smeulders is now available. Limiting factors of fast and effective classifiers for large sets of images are their dependence on the number of images analyzed and the dimensionality of the image representation. Considering the growing [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ceessnoek.info\/index.php\/convex-reduction-of-high-dimensional-kernels-for-visual-classification\/\" \/>\n<meta property=\"og:site_name\" content=\"Cees Snoek\" \/>\n<meta property=\"article:published_time\" content=\"2012-04-25T20:32:24+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2012\/04\/kerneldimensions-300x200.jpg\" \/>\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\/convex-reduction-of-high-dimensional-kernels-for-visual-classification\/\",\"url\":\"https:\/\/www.ceessnoek.info\/index.php\/convex-reduction-of-high-dimensional-kernels-for-visual-classification\/\",\"name\":\"Convex Reduction of High-Dimensional Kernels for Visual Classification - 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Limiting factors of fast and effective classifiers for large sets of images are their dependence on the number of images analyzed and the dimensionality of the image representation. 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