Image Classification using Functional Analysis and Neural Networks
(2021) STAN40 20211Department of Statistics
- Abstract
- In this thesis a number of methods for image classification are investigated. The main goal is to explore an approach based on functional data analysis and compare it with Neural Networks. The main idea is to view each image as a function and then capture the most important variation of each image class through Functional Principal Component Analysis. For comparing the methods the cifar10 dataset is used, which consist of 60000 32x32 rgb images with 10 different classes. Results indicate that the functional approach works, yielding an accuracy of 31%, where a standard Neural Network had an accuracy of 29%. A Neural Network trained on the Principal Component scores performed at 34\%. However the Convolutional Neural Networks still... (More)
- In this thesis a number of methods for image classification are investigated. The main goal is to explore an approach based on functional data analysis and compare it with Neural Networks. The main idea is to view each image as a function and then capture the most important variation of each image class through Functional Principal Component Analysis. For comparing the methods the cifar10 dataset is used, which consist of 60000 32x32 rgb images with 10 different classes. Results indicate that the functional approach works, yielding an accuracy of 31%, where a standard Neural Network had an accuracy of 29%. A Neural Network trained on the Principal Component scores performed at 34\%. However the Convolutional Neural Networks still outperformed all other methods with an accuracy of 45% when using 1D convolutions and 66% when using 2D convolutions. Lastly some possible possible improvements of the functional approach is discussed and areas for future research. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9054846
- author
- Wendsjö, Albert LU
- supervisor
- organization
- course
- STAN40 20211
- year
- 2021
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- image classification, splines, functional data analysis, eigenfunctions, neural networks
- language
- English
- id
- 9054846
- date added to LUP
- 2021-08-06 08:11:35
- date last changed
- 2021-08-06 08:11:35
@misc{9054846, abstract = {{In this thesis a number of methods for image classification are investigated. The main goal is to explore an approach based on functional data analysis and compare it with Neural Networks. The main idea is to view each image as a function and then capture the most important variation of each image class through Functional Principal Component Analysis. For comparing the methods the cifar10 dataset is used, which consist of 60000 32x32 rgb images with 10 different classes. Results indicate that the functional approach works, yielding an accuracy of 31%, where a standard Neural Network had an accuracy of 29%. A Neural Network trained on the Principal Component scores performed at 34\%. However the Convolutional Neural Networks still outperformed all other methods with an accuracy of 45% when using 1D convolutions and 66% when using 2D convolutions. Lastly some possible possible improvements of the functional approach is discussed and areas for future research.}}, author = {{Wendsjö, Albert}}, language = {{eng}}, note = {{Student Paper}}, title = {{Image Classification using Functional Analysis and Neural Networks}}, year = {{2021}}, }