Aesthetics-based Image Classification
Hiltunen, Tiitus (2020)
2020
Tieto- ja sähkötekniikan kandidaattiohjelma - Degree Programme in Computing and Electrical Engineering, BSc (Tech)
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2020-05-07
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202004294642
https://urn.fi/URN:NBN:fi:tuni-202004294642
Tiivistelmä
During the recent years, aesthetics-based image classification has been a keen research topic in computer vision.
Images can be divided into being low in aesthetic quality or high in aesthetic quality. The goal of this research is to develop methods to classify images in a similar way a human would typically do. The subjectivity of the topic creates many obstacles, such as the difficulty of dataset selection and the exactitude of finding a proper model to use.
This thesis considers popular methods used in the discipline, the most common datasets and attributes that make the problem of aesthetic-based image classification unique. Within the scope of this research, two separate convolutional neural networks based on EfficientNet were compiled and tested, one of which achieved state-of-the-art accuracy on the AVA2 dataset.
Images can be divided into being low in aesthetic quality or high in aesthetic quality. The goal of this research is to develop methods to classify images in a similar way a human would typically do. The subjectivity of the topic creates many obstacles, such as the difficulty of dataset selection and the exactitude of finding a proper model to use.
This thesis considers popular methods used in the discipline, the most common datasets and attributes that make the problem of aesthetic-based image classification unique. Within the scope of this research, two separate convolutional neural networks based on EfficientNet were compiled and tested, one of which achieved state-of-the-art accuracy on the AVA2 dataset.
Kokoelmat
- Kandidaatintutkielmat [10827]