Detekce objektů z mnoha tříd
Large Scale Object Detection
dc.contributor.advisor | Matas Jiří | |
dc.contributor.author | Novotný David | |
dc.date.accessioned | 2015-03-20T06:03:20Z | |
dc.date.available | 2015-03-20T06:03:20Z | |
dc.identifier | KOS-541574131105 | |
dc.identifier.uri | http://hdl.handle.net/10467/61273 | |
dc.description.abstract | This thesis focuses on the problem of large scale visual object detection and classification in digital images. A new type of image features that are derived from state-of-the-art convolutional neural networks is proposed. It is further shown that the newly proposed image signatures bare a strong resemblance to the Fisher Kernel classifier, that recently became popular in the object category retrieval field. Because this new method suffers from having a large memory footprint, several feature compression / selection techniques are evaluated and their performance is reported. The result is an image classifier that is able to surpass the performance of the original convolutional neural network, from which it was derived. The new feature extraction method is also used for the object detection task with similar results. | cze |
dc.description.abstract | This thesis focuses on the problem of large scale visual object detection and classification in digital images. A new type of image features that are derived from state-of-the-art convolutional neural networks is proposed. It is further shown that the newly proposed image signatures bare a strong resemblance to the Fisher Kernel classifier, that recently became popular in the object category retrieval field. Because this new method suffers from having a large memory footprint, several feature compression / selection techniques are evaluated and their performance is reported. The result is an image classifier that is able to surpass the performance of the original convolutional neural network, from which it was derived. The new feature extraction method is also used for the object detection task with similar results. | eng |
dc.publisher | České vysoké učení technické v Praze. Vypočetní a informační centrum. | cze |
dc.publisher | Czech Technical University in Prague. Computing and Information Centre. | eng |
dc.rights | A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://www.cvut.cz/sites/default/files/content/d1dc93cd-5894-4521-b799-c7e715d3c59e/cs/20160901-metodicky-pokyn-c-12009-o-dodrzovani-etickych-principu-pri-priprave-vysokoskolskych.pdf | eng |
dc.rights | Vysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://www.cvut.cz/sites/default/files/content/d1dc93cd-5894-4521-b799-c7e715d3c59e/cs/20160901-metodicky-pokyn-c-12009-o-dodrzovani-etickych-principu-pri-priprave-vysokoskolskych.pdf | cze |
dc.subject | Detekce objektů, Fisher Kernel, výběr příznaků, SVM, klasifikace obrazu, konvoluční neuronová síť | cze |
dc.title | Detekce objektů z mnoha tříd | cze |
dc.title | Large Scale Object Detection | eng |
dc.type | diplomová práce | cze |
dc.type | master thesis | eng |
dc.date.accepted | 2015-01-20 | |
dc.contributor.referee | Zitová Barbara | |
theses.degree.discipline | Počítačové vidění a digitální obraz | cze |
theses.degree.grantor | katedra kybernetiky | cze |
theses.degree.programme | Otevřená informatika | cze |
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Diplomové práce - 13133 [474]