Learning Classifiers for Multimodal Image Detection
- Publisher:
- Springer Nature
- Publication Type:
- Chapter
- Citation:
- Asia-Pacific Conference on Computer Assisted and System Engineering, 2020, 1, 15 pp. 266 - 280
- Issue Date:
- 2020
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 488270_1_En_19_Chapter_Author.pdf | Accepted Manuscript version | 937.07 kB |
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Learning Classifiers are essential analysis tools in today's era due to the rapid expansion in data-intensive applications. Every type of the classifier has own distinct architectural properties which allow to apply them in various application-specific requirements. Here, experimental work is performed to analyse the performance and accuracy of most commonly used learning classifiers applied within the scope of multimodal image classification problem space. The purpose of this study is to investigate the usefulness of the learning-classifiers with the multimodal datasets which are in many cases have the curse of dimensionality and may contain a very high volume of noise. Validation is conducted over multimodal datasets with the proposed classification algorithms operating in a parallel manner. Finally, results are discussed to explore possible ways to apply every learning classifier in multi-modal image classification applications.
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