mardi 21 avril 2015

Object detection in images (HOG)

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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:





Another problem is, sometimes the images contain several objects:


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 would be very appreciated, I just started to discover machine learning. I am using Python (scikit-image & scikit-learn).

asked 2 mins ago

Object detection in images (HOG)

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