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Machine Learning-Based Multimedia Analytics

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thesis
posted on 07.07.2020 by Daniel Mas Montserrat
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.

History

Degree Type

Doctor of Philosophy

Department

Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Edward J. Delp

Additional Committee Member 2

Jan P. Allebach

Additional Committee Member 3

Fengqing M. Zhu

Additional Committee Member 4

Qian Lin

Licence

Exports