Normalize data 'before' or 'after' regionprops?

Lim
2013 6 25
1 个回答
回答已采纳
5 次查看（30 天）

0 个投票

After image segmentation, I use regionprops to extract the features for classification. My data consists of 500 samples with 13 features. The range of the features from regionprops is averagely between 0.1 to 20,000 (area of pixel).
My problem is, is it advisable to 'norm' the value (such as im2double?) before regionprops or the normalization can be done afterwards?
I tried norm_v = v/norm(v) between the big numbers ranging between 200-20,000, but the end result (0.001 - 0.99) is still not satisfying.
Please help.

4 个评论
显示 2更早的评论 隐藏 2更早的评论

Walter Roberson
Walter Roberson 2013-6-25
Which regionprops properties are you using?
Lim
Lim 2013-6-25
编辑：Lim 2013-6-25
mean intensity, mean R mean G mean B, area, perimeter, diameter, roundness, convex hull, solidity The features that give large value are area, perimeter and diameter which gives the pixel value of the ROI
Lim
Lim 2013-7-3
Hi. My purpose of normalizing data is to scale the value to [0 1]. Is it better to do an individual 'scaling' for each feature rather than an overall scaling?
Walter Roberson
Walter Roberson 2013-7-3
It depends: is your preselection and labeling already enough to group pixels correctly, or are you using the results from regionprops to determine boundaries of the groupings but before putting them into the classifier ?

请先登录，再进行评论。

请先登录，再回答此问题。

 采纳的回答

Image Analyst
Image Analyst 2013-7-3

0 个投票

regionprops won't measure anything less than 1 pixel so I'm not sure how you're getting 0.1 pixels for the area.
I'd recommend leaving your image as integer if you can, or leaving it as whatever floating point range it has if you can. If it forces you to scale your image to 0-1 with imdouble, then you'll have to unscale it later. But again, I'd prefer not to do any scaling at all - I rarely do, but then I've never tried to send in a floating point RGB image into regionprops to get intensity information (MeanIntensity). Of course for spatial measurements (area, perimeter, equivdiameter, etc0) you don't need to scale at all since measuring those is done purely from the binary image, not from the grayscale image (single color channel).

2 个评论
显示 无 隐藏 无

Lim
Lim 2013-7-4
to Image Analyst and Walter: Actually my concern is whether the huge gap between the values will influence the performance/result of the classifier when the data is feed into the classifier. Now that I think about it, the huge gap is between different attributes and not within the same attribute, so I guess it won't affect the result? (Did I answer my own question?)
Image Analyst
Image Analyst 2013-7-4
Not sure how to answer other than to say the shape-based measurements won't depend on whether your image is in the range 0-1 or 0-255. However your intensity-based measurements will and you just need to be aware of what range your intensities are in every step of the way.

请先登录，再进行评论。

更多回答（0 个）

类别

在 帮助中心 和 File Exchange 中查找有关 Region and Image Properties 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by