EXPLAINABLE MACHINE LEARNING-BASED COMPUTER VISION APPROACHES FOR ENGINEERING VISUAL CLASSIFICATION PROBLEMS
With the development of computers and data availability, the data-driven approach of computer vision is emerging in popularity. However, the utilization of machine learning-based computer vision models has a black-box nature which is hard to justify their use in some sectors. With the still uncom...
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| Format: | Theses |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/77383 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | With the development of computers and data availability, the data-driven approach
of computer vision is emerging in popularity. However, the utilization
of machine learning-based computer vision models has a black-box nature
which is hard to justify their use in some sectors. With the still uncommon
practice of qualitative evaluation, this thesis demonstrates the importance of
feature-highlight investigation of the machine learning-based approach. This
thesis compares three machine learning methods for image classification, which
are principal component analysis, convolutional neural network, and capsule
network, for solving three engineering cases. This thesis also highlights the
insights gained from principal component analysis and investigates the compatibility
of explainability methods with capsule networks. Out of the three
techniques, it is found that the capsule network models are considered to be the
best models in the three cases solved. It is also found that principal component
analysis provides insight into the problem’s difficulty for all the data-driven
approaches. By observing the results, it is found that the capsule network does
not compatible with sensitivity analysis for explainability. |
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