- Title
- Developing a Machine Learning Algorithm for Outdoor Scene Image Segmentation
- Creator
- Zangwa, Yamkela
- Subject
- Computational intelligence Computer science
- Date Issued
- 2020
- Date
- 2020
- Type
- Thesis
- Type
- Masters
- Type
- MSc (Computer Science )
- Identifier
- http://hdl.handle.net/10353/12087
- Identifier
- vital:39150
- Description
- Image segmentation is one of the major problems in image processing, computer vision and machine learning fields. The main reason for image segmentation existence is to reduce the gap between computer vision and human vision by training computers with different data. Outdoor image segmentation and classification has become very important in the field of computer vision with its applications in woodland-surveillance, defence and security. The task of assigning an input image to one class from a fixed set of categories seem to be a major problem in image segmentation. The main question that has been addressed in this research is how outdoor image classification algorithms can be improved using Region-based Convolutional Neural Network (R-CNN) architecture. There has been no one segmentation method that works best on any given problem. To determine the best segmentation method for a certain dataset, various tests have to be done in order to achieve the best performance. However deep learning models have often achieved increasing success due to the availability of massive datasets and the expanding model depth and parameterisation. In this research Convolutional Neural Network architecture is used in trying to improve the implementation of outdoor scene image segmentation algorithms, empirical research method was used to answer questions about existing image segmentation algorithms and the techniques used to achieve the best performance. Outdoor scene images were trained on a pre-trained region-based convolutional neural network with Visual Geometric Group-16 (VGG-16) architecture. A pre-trained R-CNN model was retrained on five different sample data, the samples had different sizes. Sample size increased from sample one to five, to increase the size on the last two samples the data was duplicated. 21 test images were used to evaluate all the models. Researchers has shown that deep learning methods perform better in image segmentation because of the increase and availability of datasets. The duplication of images did not yield the best results; however, the model performed well on the first three samples.
- Format
- 76 leaves
- Format
- Publisher
- University of Fort Hare
- Publisher
- Faculty of Science and Agriculture
- Language
- English
- Rights
- University of Fort Hare
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UFH Department of Computer Science
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Zangwa_201209442_ComputerScience.pdf | 3 MB | Adobe Acrobat PDF | View Details Download |