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Introduction to Image Segmentation and Clustering

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Clustering Techniques for Image Segmentation

Abstract

Image segmentation’s role in image processing represents and describes an image’s different information into a simpler and understandable format. Usually, image segmentation is the initial step of a higher level operation in an image-processing system. Labeling and tracking of objects are examples of the higher level operation that is usually performed after image segmentation. Image segmentation step can segment a data with one (a.k.a., 8 bits gray scale data) or three dimensions (a.k.a., 24 bits color data). The use of two different types of data for image segmentation has its benefits and limitations. For instance, the 8 bits data is less prone to noise, while 24 bits data has additional information. Image segmentation is much more complex than an image classification to classify data or tracking object in practice. Thus, this chapter begins by explaining the concept of image segmentation and then compares its working and benefits with image classification. Of segmentation techniques, the clustering needs very little prior information of an image to segment it. Few other segmentation techniques are widely used for image segmentation, and comparison between the well-known segmentation techniques is presented in this chapter.

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Authors and Affiliations

  1. CQUniversity, Melbourne, Victoria, Australia

    Fasahat Ullah Siddiqui

  2. BIUST, Botswana International University of Science and Technology, Palapye, Botswana

    Abid Yahya

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  1. Fasahat Ullah Siddiqui
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  2. Abid Yahya
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Siddiqui, F.U., Yahya, A. (2022). Introduction to Image Segmentation and Clustering. In: Clustering Techniques for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-030-81230-0_1

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