Automatic Image Annotation for Microstock Sites – Bc. Michal Červeňanský
Bc. Michal Červeňanský
Master's thesis
Automatic Image Annotation for Microstock Sites
Automatic Image Annotation for Microstock Sites
Abstract:
Táto práca sa zameriava na problém anotácie obrázkov vo fotobankovom priemysle. Na riešenie problému využíva najmodernejšie techniky strojového učenia pre detekciu objektov a klasifikáciu obrazu. Anotačný postup, optimalizovaný pre získanie anotácie v podobe kľúčových slov pre využitie vo fotobankách, je postavený na existujúcom nástroji MUFIN Annotation Framework. Pôvodnú presnosť anotácie MUFIN sa …moreAbstract:
This thesis addresses the issue of image annotation for the microstock industry. It attempts to bridge the gap between a real-life problem of image annotation and the state-of-the-art research of object detection and image classification techniques. Building upon the existing MUFIN Annotation Framework, we develop an annotation pipeline optimized for obtaining keyword annotation for microstock usage …more
Language used: English
Date on which the thesis was submitted / produced: 18. 5. 2021
Identifier:
https://is.muni.cz/th/gisob/
Thesis defence
- Date of defence: 25. 6. 2021
- Supervisor: RNDr. Petra Budíková, Ph.D.
- Reader: RNDr. Michal Batko, Ph.D.
Citation record
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsMaster programme / field:
Artificial intelligence and data processing / Big data
Theses on a related topic
-
Automatic Recognition of User Interface States Using Convolutional Neural Networks
Klára Petrovičová -
Modelling small-RNA binding using Convolutional Neural Networks
Eva Klimentová -
Visualization of hidden layers in convolutional neural networks
Jakub Hruška -
Segmentation of Dense Cell Populations using Convolutional Neural Networks
Filip Lux -
Interpretation techniques for deep neural networks in digital histopathology
Martin Krebs -
Automatic trackingand assessment of chronic wounds using augmented skin imaging and convolutional neural networks
Monika Molnárová -
Attention Based High Resolution Image Classification
Dominik HEINDL -
Automatic Image Classification
Lukáš KÖLBL