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Hi,
I am working on image classification based on colour histograms. I have 4 classes of images. Each class of images have a different colour dominant in them. For each image I have HSV histogram. The HSV histogram is made from 8 bins for Hue, 2 bins each for Saturation and Value. Therefore each image has a vector of 32 data.
I have 65 training images in each of the 4 classes: total of 260 images.
I need to feed the 32x260 matrix to train the neural network.
My objective is to compare two methods in classification. One is classifying the image as a whole. Other is dividing an image into blocks and classifying them individually, then finally based on some voting method or some other method classify the image as a whole.
I have basically two questions in carrying out this process.
1. Does having 8x2x2=32 HSV histograms suffice for the training process?
2. For the second method of classification, do I have to perform training separately on the individual blocks? Or can I use the trained network in the first method(whole image classification) for classifying the blocks?
Some advice would be helpful, please.
Thanks,
Vashini
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