For this image, we may find there are many interest points.
These interest points we call visual primitives.
Every visual primitives can be described by a set of visual features or
you can think is a high dimension feature vector.
And then, each image is a collection of visual primitives.
Then, we will study how to turn those visual primitives into patterns and
how to do pattern minning on large number of images.
So that means we want to study visual pattern discovery problem.
For visual pattern discovery, we can think about it from this images.
We can use feature extraction.
We can extract lots of high dimension features, they form visual primitives.
Those visual primitives can be clustered,
based on their space they may cluster into visual items, okay?
Those visual items, if they are similar, then they belong to the same item.
You can map them into different item ID, for example.
You can map this feature into W, and
maybe another picture I have of similar one, they actually also called W, okay?
Then each visual primitive, they can have, based on that k-nearest-neighbor,
they can form small clusters, and those we usually called them a transaction.
For example, you may find these three visual primitives
can form a transaction like CBA as a transaction.
Then an image may have many transactions, okay, in every image.
Then if there are many, many images, you may, based on their transactions,
you do the frequent pattern mining, you will be able to find some visual patterns.
For example, in these pictures, you may find a BA and CA,
actually are quite frequent together in those transactions.
So you can say, B and CA or CBA are frequent patterns,
are the frequent visual patterns in those images.