What text or tutorials would you recommend for learning machine learning for image processing? I'm searching a good (and compact) book or online tutorial about multivariate pattern analysis in images with machine learning techniques. I took a machine learning course based on the Bishop text book "Pattern Recognition and Machine Learning" from 2006 but I found it difficult and is currently out of date.
Ideally the book would be oriented to practical applications and related to multivariate pattern analysis in images (image processing). The mathematical foundations and proofs are not important. Most important is that it present a variety of different algorithms and techniques are presented (e.g. SVM etc.) in an easy way with some explanations and perhaps implementation details.
Can someone suggest a good book?
What about the the book "Pattern Classification" from R. Duda, P. Hart, and D. Stork?
 A: I would recommend Computer Vision:  Models, Learning, and Inference by Simon Prince
the pdf is available (free) to students at the above link. It is exactly aiming at combining ML and image processing
A: The book by Prince, recommended by @seanv507 is indeed an excellent book on the topic (+1). And while it is not really compact, it has very logical structure and even a generous refresher chapter on probability as well as great focus on machine learning within computer vision context.
However, I'd like to recommend another excellent book on the topic (also freely downloadable), which, while having more focus on computer vision per se, IMHO contains enough machine learning material to qualify for an answer. The book that I'm talking about is "Computer Vision: Algorithms and Applications" by Richard Szeliski (Microsoft Research). One of the advantages of this book versus the one by Price is... narrower margins, which allow for larger font size and, thus, better readability. Also, the book by Szeliski is very practical. Since both books share significant content, but have somewhat different focus, in my opinion, they very well complement each other. All this, among other advantages, makes it very easy for me to highly recommend Szeliski's book.
