Convolutional neural networks for classifying studio/non-studio frames in TV news programs
This project has studied an unusual topic on image recognition and classification that can have numerous utilities on multimedia content. The approach studied on this project has been scenes recognition on TV news programs, which is a good starting point to distinguish scenes on visual content displ...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/327131 |
| Acceso en línea: | https://hdl.handle.net/2117/327131 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine translating Neural networks (Computer science) Convolutional Neural Networks Deep Learning Machine Learning Python Keras Image processing Video analysis Aprenentatge automàtic Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | This project has studied an unusual topic on image recognition and classification that can have numerous utilities on multimedia content. The approach studied on this project has been scenes recognition on TV news programs, which is a good starting point to distinguish scenes on visual content displayed on TV. The work in this project can be the first steps to segmentscenes on movies, series, documentaries, etc... The methodology used to build a program capable to recognise and classifyimages is Deep Learning, a subset of Machine Learning based on artificialneural networks that learn from representations of data obtained after performing some operations on the input images. As in Machine Learning, atraining process with multiple example outputs is necessary to make the system capable to figure out the rules to obtain those outputs (categories) from the given inputs (frames). To build an image recognition system, the most suitable framework (previous comparison has been made between some of them) has been installed to code the operations that lead to the image classification andthe results to evaluate its performance. Another important part to complete this project is the data collection and management, the data volume has to be large and diverse in order to increase the number and type of patterns or features learnt from the representations, and therefore, to increase the system’s capability to generalize. It also has to be managed properly, in order to not difficult the learning process with additional, human errors. The last part of this project consists on training the classifier in the different architectures, check which input parameters lead to the best performance and compare the results to decide which of them is the most appropriate to classify TV news program images. After the training is finished, the system must be tested with never-seen data in order to check its real performance and obtain a proper evaluation. |
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