I would also suggest the book "computer vision" by Richard Szeliski.
For you classification problem it really depends on what you want as
output and what the statistics of the data are.
If I understand you correctly, you want a prediction for each label. If
your images are somewhat natural, the labels
are usually smooth in the sense that neighboring regions are often
labelled the same way.
Then it might be helpful to use an oversegmentation of the image into
superpixels (for example using slic from scikit-image)
and then classify on a superpixel basis.
Each superpixel could be described by descriptors as mentioned by Joseph.
I have been planing to write a tutorial on that for a long time :-/
What are the classes? Are they semantic?
A completely different approach would be to use image-based random
forests, such as implemented here:
https://github.com/deeplearningais/curfil
Hth,
Andy
On 10/22/2013 02:35 PM, jim vickroy wrote:
On 10/22/2013 3:32 PM, Joseph Jacobs wrote:
The best book I have come across for image processing/vision +
machine learning is one by Simon Prince. You can download the book
from his website (http://computervisionmodels.com/). Chapter 13 gives
a good intro to feature extraction.
OK, great -- just what I need! --jv
Joe
On 22 Oct 2013, at 22:27, jim vickroy wrote:
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification
pixel-wise would not be ideal (please correct me if I am wrong). I
think the better way would be to perform some sort of feature
extraction on the image (eg. SIFT, SURF, HOG, LBP and many, many
more...checkout scikit-image or google it) and to do classification
using that. Which feature extraction method would be ideal for you
and how you apply it would depend on the application.
Thanks for the suggestion; I'll look into that! I'm a novice, but I
agree pixel-by-pixel classification would not seem to scale well. --jv
Not sure how helpful that was.
Joe
On 22 Oct 2013, at 21:10, jim vickroy wrote:
Hi,
Apologies if this is an inappropriate question for this forum.
I have a collection of (1024x1024) mono-chromatic images in which
each pixel is to be labeled as 1 of several categories (e.g.,
10). Furthermore, each mono-chromatic image was captured through
several filters (e.g., 5).
My understanding of the sci-kit documentation is that I would
train a classifier on a pixel-by-pixel basis and then apply it, to
new images, on a pixel-by-pixel basis. Is that correct?
Thanks for your time.
-- jv
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