I have some background in machine learning and I also just completed a face-identification excersize using support vector machine. I am in the process of trying to convert this exercise to HMM, but I am having problems understanding the notation and how to use it (I am using Kevin Murphy’s HMM package).
I am given about a 50 gray scale images of 6 different people (numbered 1-6). Each image is a 10 pixels by 10 pixels and each pixel can have values between 0-255 (8 bit gray scale). The goal is that I will be able to classify a new image to one of the 6 faces.
My approach is to take each image and make it a long vector of length 100 elements each is a pixel value . Now, I am getting to the confusing part. The notations I am using is as follows:
N : Number of observation symbols - I understand that the hidden state is the person’s face (i.e 1-6), therefore, there are 6 hidden states so N=6.
T : Length of observation sequence – is this equal to a 50 ? I am not sure what this represents
M: Number of observation symbols – is this equal to a 100 ? Does the term of “observation symbol” refer to the number of elements in the vector representing the observation?
O : Number of observations – what does this represent? In every example they use a single binary observed value and they make this to be 2 (i.e on or off). What would this be in my case ?
I greatly appreciate the help
