dc.contributor.advisor | Antonio Torralba. | en_US |
dc.contributor.author | Biswas, Aritro | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2018-12-18T19:48:17Z | |
dc.date.available | 2018-12-18T19:48:17Z | |
dc.date.copyright | 2018 | en_US |
dc.date.issued | 2018 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/119746 | |
dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. | en_US |
dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 69-71). | en_US |
dc.description.abstract | In this thesis, I developed machine learning methods that would support tools for gaining health insights from social media images. On the one hand, I have helped create a dataset for food image segmentation and a segmentation network for this task, a task that is very relevant for understanding the nutritional content of food from images. I also explored the flip side of the issue, and helped design an interface that users can use to explore food recipes in machine learning-driven ways. On the other hand, I helped create a dataset and model for classifying types of disasters given images of natural or manmade disasters. | en_US |
dc.description.statementofresponsibility | by Aritro Biswas. | en_US |
dc.format.extent | 71 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Using computer vision to gain health insights from social media | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.oclc | 1078690125 | en_US |