Feature extraction for classification

Hi there, I attached the images that I want to extract features for classification. these are zoomed images of different fruits. kindly help me how to find features on these images. I tried using extractHOGfeatures function but its not giving me correct classification.

2 Comments

What are the classes? How many classes are in each photo? One? If so, then one feature may simply be the color. Why do you think HOG should be what you want? Can you attach what you think the classification should look like?
I have 10 classes for 10 different fruits.Each fruit have 50 samples for training. Then for testing I have similar looking test dataset. Classification should identify the given input fruit name.

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 Accepted Answer

I would think that just color (Hue and Saturation) and texture might be enough. Of course if two images look the same but are different fruits, there is no telling, like you have an image of orange grainy things and one is an apricot and one is a nectarine.

3 Comments

It will be helpful if you eleborate little further with an example.
For example, convert to HSV color space and get the mean hue, saturation, and value.
hsvImage = rgb2hsv(rgbImage);
meanHue = mean2(hsvImage(:,:,1))
meanSat = mean2(hsvImage(:,:,2))
meanValue = mean2(hsvImage(:,:,3))
sdImage = stdfilt(hsvImage(:,:,3)); % Std Deviation of Value channel.
meanStdDev = mean2(sdImage);
You can make a feature vector for this one image like
featureVector = [meanHue, meanSat, meanValue, meanStdDev];

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More Answers (1)

I believe the following example will be your help. Using this method, you can extract 4096-dimensional feature vector for each image.

1 Comment

Ushna Asif
Ushna Asif on 21 Sep 2017
Edited: Ushna Asif on 21 Sep 2017
I'm using Matlab 2015a and this function alexnet is not available in this version. I tried downloading this using alexnet function in command window which suppose to provide a downloading link but it says undefined function or variable.

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