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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