CODEWITHSUNDEEP
image-processingfeature-extraction
Encinaar
Encinaar

Reputation: 149

Effects of image quality/resolution in feature extraction

I am working on a project which identifies objects after capturing their images on Android platform. For this, I extracted features of sample images such as compactness, rectangularity, elongation, eccentricity, roundness, sphericity, lobation, and hu moments. After then, random tree is used as classifier. As I used pictures gathered from Google which are not in high resolution for creating my classifier, captured images of size 1280x720 gives 19/20 correct results when the image is cropped.

However, when I capture images of large sizes such as about 5 megapixels, and crop them for identification, the number of obtained correct results dramaticaly decreases.

Do I need to extract features of images with high resolution and train them in order to get accurate results when high resolution pictures are captured? Is there a way of adjusting extracted features related to the image resolution?

Upvotes: 2

Views: 465

Answers (1)

mpenkov
mpenkov

Reputation: 21904

Some feature descriptors are sensitive to scaling. Others, like SIFT and SURF, are not. If you expect the resolution (or scale) of your images to change, it's best to use scale-invariant feature descriptors.

If you use feature descriptors that are not scale-invariant, you can still get decent results by normalizing the resolution of your images. Try scaling the 5 megapixel images to 1280x720 -- do the classification results improve?

Upvotes: 2

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