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If you took a book and read a stream of characters or words streaming into the the system.
You can find repeating patterns of words or characters. The idea is to build up a data base of repeating characters, words, paragraphs,
pages, and then books.
Starting with common pieces, and then to the more rarer arrangements of of common pieces. Like many common letter and the
the rare novel.
You have two search pointers Target pointer is compared to source pointer.
Kolmogorov Complexity:
https://en.wikipedia.org/wiki/Kolmogorov_complexity In video or images, or computer vision, there is the Region Of Interest, ROI. I call this the focus:
https://www.learnopencv.com/how-to-select-a-bounding-box-roi-in-opencv-cpp-python/ The both organic eyes can mechanically focus on to one region of interest, ROI. All within is enhanced and all outside
blurred out.
To extract common pieces out of a monolithic wall of data clutter, a program uses, two pointers that move through recorded data.
These are two focuses.
The idea is to find two of the same object that have completely different back grounds.
If you take two completely different images with the same one object in both, and make the images transparent. Then over lap
both image and move it around until both same objects overlap. With object found in the over lapping images, The clutter outside
focus or ROI is clutter wall of confusion. The found object is cut away from selected non chaotic area, of the MAIN FOCUS.
The capture of the object is record into a data base.
If you took exactly the same two images and made them transparent and overlap them then that images, then a capture of
the whole image would occur.
But if it is a image of a special location, you saw on a trip to a national park, then it is a rare image that cannot be used as a
building blocks for other images. Very inflexible object capture.
Unsupervised learning is about finding repeating sub features, objects, arrangements of the smaller pieces. matching pictures,
repeating sub sequences. and sequence built out of smaller sequences. From raw data.
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