Abstract
The purpose of this chapter is to introduce in a fairly concise manner the key ideas underlying the field of unsupervised learning from the perspective of clustering for image segmentation tasks. We begin with a briefly review of fundamental concepts in clustering and a quick tour of its four basic models, namely, partitioning-based, hierarchical, density-based, and graph-based approaches. This is followed by a short introduction to distance measures and a brief review on performance evaluation metrics of clustering algorithms. This introduction is necessarily incomplete given the enormous range of topics under the rubric of clustering. The hope is to provide a tutorial-level view of the field so that many topics covered here can be delved more deeply into and state-of-the-art research will be touched upon in the next four chapters.
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Wang, X., Wang, X., Wilkes, D.M. (2020). Unsupervised Learning for Data Clustering Based Image Segmentation. In: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment. Springer, Singapore. https://doi.org/10.1007/978-981-13-9217-7_4
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