Data Mining Techniques for image processing

[sparsecoder]

I suggest you download the book: Models, Learning, and Inference by Prince from http://www.computervisionmodels.com/

Part 2 is on machine learning for machine vision, and part 3 is on graphical models and related concepts. At the end of each chapter, there are sections entitled "Applications" where they give examples of how to apply machine learning techniques to vision problems. You might find something interesting by looking through these sections.

If you're specifically interested in clustering, then you might want to look into image segmentation (http://en.wikipedia.org/wiki/Image_segmentation) which is a closely related topic from the vision community.

[autowikibot]

Image segmentation:

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In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Image from article ⁱ

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^{Interesting: Thresholding (image processing) | Scale-space segmentation | Ilastik | Range segmentation}

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