# Simple way for histograms classification

I'm trying to classify a histogram. I have 4 classes and I generate 4 histograms (h1, h2, h3 and h4) for each class. Each histogram contains 10 bins (attributes describing an object) on the x-axis and the frequencies on the y-axis. The problem is: given a new histogram (hn), find to which class it belongs.

My question; is there any simple classifier which can train based on the 4 predefined histograms and classify any new given histogram? and is there a Matlab implementation?

• If you have a single histogram by class, you can run a chi-square test on the bin frequencies for each class. – Xi'an Apr 16 '15 at 15:15
• currently I have one histogram for each class, but some class may have more than one histogram. So, I need a classifier can deal with that. – Omar14 Apr 16 '15 at 15:21

You can use nearest neighbor classification, with an appropriate distance metric. For example histogram intersection distance, $\chi^2$ distance, F-divergence, jensen-shannon divergence, or any other of the divergence measures you like.