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I'm searching a good (and compact) book about multivariate pattern analysis in images with machine learning techniques. I took a machine learning course and used for it the Bishop book but I found it not so great (sometimes difficult).

Now, I don't need the book for a lecture but for a project, so the book should perhaps be more practical oriented and related to multivariate pattern analysis in images (image processing). Proofs are not important. More important is that different algorithms/techniques are presented (e.g. SVM etc.) in an easy way with some explanations and perhaps implementation details.

Does somebody know a good book?

What about the the book "Pattern Classification" from R. Duda, P. Hart, and D. Stork?

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    $\begingroup$ I suggest you change from ML to Machine learning in the title. ML is often used for maximum likelihood. $\endgroup$ – Richard Hardy Apr 11 '15 at 18:49
  • $\begingroup$ I would make the title more precise: your question is on using machine learning for image processing - otherwise it is a duplicate of: stats.stackexchange.com/questions/12386/… $\endgroup$ – Tim Apr 11 '15 at 20:20
  • $\begingroup$ edited title to be more precise $\endgroup$ – seanv507 Apr 12 '15 at 10:28
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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

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  • $\begingroup$ Thank you very much. In detail, I have to do multivariate pattern analysis of images. Is this book also ok with this? $\endgroup$ – machinery Apr 12 '15 at 19:59
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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.

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    $\begingroup$ Thank you, I will have a look at the book, but of course I cannot read a lot of books. I think I will read one book from the beginning to the end and from other books only some chapters for new techniques. So I have to decide on one book. $\endgroup$ – machinery Apr 12 '15 at 20:01
  • $\begingroup$ @user1684118: You're welcome and good luck! $\endgroup$ – Aleksandr Blekh Apr 12 '15 at 23:31

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