Javascript must be enabled for the correct page display

The Effect of pooling the Hidden Activation in a Patch-Based Image Classification System using Multi-Layer Perceptrons

Ruijter, R.J.A. de (2017) The Effect of pooling the Hidden Activation in a Patch-Based Image Classification System using Multi-Layer Perceptrons. Bachelor's Thesis, Artificial Intelligence.

[img]
Preview
Text
AI_BA_2017_Rogier_de_Ruijter.pdf - Published Version

Download (420kB) | Preview
[img] Text
Toestemming.pdf - Other
Restricted to Backend only

Download (77kB)

Abstract

In this thesis a patch-based image classification system using a multi-layer perceptron (MLP) system is proposed. The patch-based MLP is trained on a set of randomly selected patches from the training images. This method is also used in a design where multiple patch-based MLPs are trained on specific regions of the images, this is called the scoped patch-based MLP system. Both the patch-based MLP and the scoped patch-based MLP system are used as a feature extractor for a classifier as well. The performance of these approaches are tested on the MNIST and CIFAR-10 datasets, where 99.43% and 71.63% classification accuracy are obtained, respectively.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 08:26
Last Modified: 15 Feb 2018 08:26
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/14828

Actions (login required)

View Item View Item