Which Features extraction method will be more accurate for classifying the images based on their Clarity?

asked 2018-08-22 02:40:03 -0500

Samjith888 gravatar image

updated 2018-08-22 04:17:13 -0500

I want to classify Images into Good clarity or Bad Clarity (Clarity Check) by using SVM. I have used the HOG feature extraction method, but its accuracy is very poor.which feature extraction method will be suitable for image clarity check? Googled image image Googled googled image

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can you explain "Clarity Check" ? also, maybe a (small) example image wouldbe helpful

berak gravatar imageberak ( 2018-08-22 03:05:17 -0500 )edit

@berak : Clarity check means that the image is classifying into good or bad based on their Clarity. Bad images will be blurred, color faded or shaded images, Noisy images (quality of the image will be poor, so user can't work on detecting ROI from such images).

Samjith888 gravatar imageSamjith888 ( 2018-08-22 04:03:48 -0500 )edit

indeed hog features can't solve this. maybe have a look at algorithms like "BRISQUE" or "Naturalness Image Quality Evaluator(NIQE)"

berak gravatar imageberak ( 2018-08-22 23:52:03 -0500 )edit

@berak : I have also used HOH with SVM to classify whether a image contains human or not .The images were captured from roof and some images were captured from side wall ,hence the images didn't contain the full body of the human. I trained some images that contains human and some images without human by using HOG and SVM. (training database =30 images for each)..But the output is poor.. Is this not the right method to classify the task ?

Samjith888 gravatar imageSamjith888 ( 2018-08-23 07:26:14 -0500 )edit

and how is that related to your question ?

berak gravatar imageberak ( 2018-08-23 07:49:48 -0500 )edit
1

and expecting something from 30 images only is outright silly.

you need like thousands (see e.g. INRIA database)

berak gravatar imageberak ( 2018-08-23 07:51:38 -0500 )edit