Object detection in images (HOG)
Snowflake
luecks at gmail.com
Wed Apr 22 03:19:12 EDT 2015
Hi!
I am new to machine learning and I need some help.
I want to detect objects inside cells of microscopy images. I have a lot of
annotated images (app. 50.000 images with an object and 500.000 without an
object).
So far I tried to extract features using HOG and classifying using logistic
regression and LinearSVC. I have tried several parameters for HOG or color
spaces (RGB, HSV, LAB) but I don't see a big difference, the predication
rate is about 70 %.
I have several questions. How many images should I use to train the
descriptor? How many images should I use to test the prediction?
I have tried with about 1000 images for training, which gives me 55 %
positive and 5000, which gives me about 72 % positive. However, it also
depends a lot on the test set, sometimes a test set can reach 80-90 %
positive detected images.
Here are two examples containing an object and two images without an object:
Object01 <http://labtools.ipk-gatersleben.de/ML/with_object01.jpg>
object02 <http://labtools.ipk-gatersleben.de/ML/with_object03.jpg>
cell01 <http://labtools.ipk-gatersleben.de/ML/cell01.jpg>
cell02 <http://labtools.ipk-gatersleben.de/ML/cell02.jpg>
Another problem is, sometimes the images contain several objects:
objects <http://labtools.ipk-gatersleben.de/ML/with_object02.jpg>
Should I try to increase the examples of the learning set? How should I
choose the images for the training set, just random? What else could I try?
Any help or tips would be very appreciated, thank you very much in advance!
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