<pre>Machine learning is widely used to extract meaningful information from video, images, audio, text, and other multimedia data. Through a hierarchical structure, modern neural networks coupled with backpropagation learn to extract information from large amounts of data and to perform specific tasks such as classification or regression. In this thesis, we explore various approaches to multimedia analytics with neural networks. We present several image synthesis and rendering techniques to generate new images for training neural networks. Furthermore, we present multiple neural network architectures and systems for commercial logo detection, 3D pose estimation and tracking, deepfakes detection, and manipulation detection in satellite images.<br></pre>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12616292 |
| Date | 07 July 2020 |
| Creators | Daniel Mas Montserrat (9089423) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Machine_Learning-Based_Multimedia_Analytics/12616292 |
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