Where do data mining and computer vision converge? I am traditionally a computer vision guy, and have started doing some self-learning in data mining.  I'm having a hard time finding places where these 2 fields converge, as I'd like to get a self-study project that relates the two fields.
where are these areas two used together?
 A: I have seen some work in which researchers are able to harness the availability of large amounts of image data from the web to perform complex tasks (e.g. similarity search).
Two excellent examples I can think of are this paper and the work by James Hays.
A: Well, there are various places. For example, indexing image data is a natural source of convergence between data mining and computer vision. In order to cluster images, you will probably want to have a tight interplay of computer vision and data mining, too.
Or for example Robust Segmentation of Relevant Regions in Low Depth of Field Images, an image segmentation method that is in fact based on DBSCAN clustering. I bet you can come up with many such examples when you look around. Say: aligning a stack of panorama images requires actually some kind of data mining, too. At least when you have a few thousand SIFT keypoints to work with.
A: The most prominent example is the HMAX model. The model uses a series of layers to produce a single vector feature set that is composted of the data needed to classify objects using machine learning. In order to achieve in-variance (Rotation, Size, Scale) in this model the developer needs to have many pictures of the same thing or person which can be used to train a classifier.
When researchers are attempting to find better ways of creating or reading these features, that's where computer vision meets data mining.
