CODEWITHSUNDEEP
computer-visionhistogramconv-neural-networkfeature-extraction
Addee
Addee

Reputation: 671

Comparison of HoG with CNN

I am working on the comparison of Histogram of oriented gradient (HoG) and Convolutional Neural Network (CNN) for the weed detection. I have two datasets of two different weeds.
CNN architecture is 3 layer network.

1) 1st dataset contains two classes and have 18 images. The dataset is increased using data augmentation (rotation, adding noise, illumination changes) enter image description here

Using the CNN I am getting a testing accuracy of 77% and for HoG with SVM 78%.

2) Second dataset contact leaves of two different plants. each class contain 2500 images without data augmentation.
enter image description here

For this dataset, using CNN I am getting a test accuracy of 94% and for HoG with SVM 80%.

My question is Why I am getting higher accuracy for HoG using first dataset? CNN should be much better than HoG.

The only reason comes to my mind is the first data has only 18 images and less diverse as compare to the 2nd dataset. is it correct?

Upvotes: 2

Views: 6799

Answers (1)

tsh
tsh

Reputation: 2375

Yes, your intuition is right, having this small data set (just 18 images before data augmentation) can cause the worse performance. In general, for CNNs you usually need at least thousands of images. SVMs do not perform that bad because of the regularization (that you most probably use) and because of the probably much lower number of parameters the model has. There are ways how to regularize deep nets, e.g., with your first data set you might want to give dropout a try, but I would rather try to acquire a substantially larger data set.

Upvotes: 1

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