{"id":1826,"date":"2017-03-17T14:22:00","date_gmt":"2017-03-17T19:22:00","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/alina-zare\/?p=1826"},"modified":"2026-02-18T11:29:02","modified_gmt":"2026-02-18T16:29:02","slug":"cobb2017multiple","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2017\/03\/17\/cobb2017multiple\/","title":{"rendered":"Multiple-instance learning-based sonar image classification"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the &#8220;instances&#8221; and the sonar images are defined as the &#8220;bags&#8221; within the MILES classification framework. The intensity feature distributions are discrete while the texture feature distributions are continuous, thus the Cauchy-Schwarz divergence metric is used to embed the instances in a higher-dimensional discriminatory space. Results are given for labeled synthetic aperture sonar (SAS) image database containing images with a variety of seabed textures. <\/p>\n<h2>Links:<\/h2>\n<p> <a href=\"\/\/doi.org\/10.1117\/12.2262530\"><img decoding=\"async\" border=\"2\" alt=\"\u201cSPIE\" height=\"50\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/SPIE-logo-cmyk-e1482256584489.jpg\"><\/a> <a href=\"https:\/\/github.com\/GatorSense\/Publications\/blob\/master\/Cobb2017Multiple.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><\/p>\n<h2>Citation:<\/h2>\n<pre><code>J. T. Cobb, X. Du, A. Zare and M. Emigh, \"Multiple-instance learning-based sonar image classification,\" in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017. <\/code><\/pre>\n<pre><code>@InProceedings{Cobb2017Multiple,\nTitle = {Multiple-instance learning-based sonar image classification},\nAuthor = {Cobb, James T. and Du, Xiaoxiao and Zare, Alina and Emigh, Matthew},\nBooktitle = {Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII},\nYear = {2017}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the &#8220;instances&#8221; and the sonar images are defined as the &#8220;bags&#8221; within the MILES classification framework. [&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":[53,151,367,487,659,717,729],"class_list":["post-1826","post","type-post","status-publish","format-standard","hentry","category-conference_paper","tag-uncertain-imprecise-labels","tag-classification","tag-image-processing","tag-multiple-instance","tag-segmentation","tag-superpixel","tag-synthetic-aperture-sonar"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1826","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=1826"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1826\/revisions"}],"predecessor-version":[{"id":14981,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/1826\/revisions\/14981"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=1826"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=1826"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=1826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}