{"id":1160,"date":"2015-05-11T15:19:54","date_gmt":"2015-05-11T20:19:54","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1160"},"modified":"2026-02-18T11:28:01","modified_gmt":"2026-02-18T16:28:01","slug":"zare2015multiple","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2015\/05\/11\/zare2015multiple\/","title":{"rendered":"Multiple instance dictionary learning for subsurface object detection using handheld EMI"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific training data samples that correspond to target and non-target are not known and difficult to determine manually. In this paper, a dictionary learning method that addresses this issue using the multiple instance learning techniques is presented. Results are shown on real EMI data sets.<\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"\/\/doi.org\/10.1117\/12.2179177\"><img decoding=\"async\" border=\"2\" alt=\"SPIE\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/SPIE-logo-cmyk-e1482256584489.jpg\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/zare2015multiple.pdf\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/pdflogo-e1482256801729.png\" height=\"50\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/FUMI\"><img decoding=\"async\" border=\"2\" alt=\"PDF\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/GitHub-Mark-e1482256611783.png\" height=\"50\"><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>A. Zare, M. Cook, B. Alvey, and D. Ho, \u201cMultiple instance dictionary learning for subsurface object detection using handheld EMI,\u201d in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.<\/code><\/pre>\n<pre><code>@InProceedings{zare2015multiple,\nTitle = {Multiple instance dictionary learning for subsurface object detection using handheld EMI},\nAuthor = {Alina Zare and Matthew Cook and Brendan Alvey and Dominic Ho},\nBooktitle = {Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX},\nYear = {2015},\nMonth = {May},\nNumber = {94540G},\nVolume = {9454},\nDoi = {10.1117\/12.2179177},\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: A dictionary learning approach for subsurface object detection using handheld electromagnetic induction (EMI) data is presented. A large number of unsupervised and supervised dictionary learning methods have been developed in the literature. However, the majority of these methods require data point-specific labels during training. In the application to subsurface object detection, often the specific [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[53,451,487,733],"class_list":["post-1160","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-uncertain-imprecise-labels","tag-metal-detector","tag-multiple-instance","tag-target-detection"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1160","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=1160"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1160\/revisions"}],"predecessor-version":[{"id":14615,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1160\/revisions\/14615"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1160"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1160"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}