# Getting OpenGM to work …

I had been trying hard to get accustomed with the OpenGM library so that I get to understand what are the things that I will be needing for my implementation of Approximate algorithms.

I will jot down my findings here:

Below is the most important image that will help me to write off things quite cleanly.

The things that are needed by the OpenGM for completely specifying the above factor model are:

1. The number of variables along with cardinality (the number of labels) of each of these. This is done by constructing an object called an Label Space.
2. Functions describe the different small $\varphi_i$ that are used to decompose the bigger $\varphi$.
3. Operations can be addition, multiplication, max and min. These are the operations through which the $\varphi$ decomposes into $\varphi_i$.
4. Factors is the only missing link which connects the appropriate $\varphi$ with respective $x_v's$ intended as the inputs.

Writing a complete model in OpenGM using ExplicitFunctions.

The above one was a very primitive model. Let us make from a complete one like present below:

Gallery

# Woah! Finally Perceptron has ended

For last 3 months,  me and my two other friends  worked hard for this event. We needed to provide our juniors a platform from where they can learn. The gluttony of the events held presently in Prastuti always made me sad. To get over all these things, we brought Perceptron.

Perceptron is a event based on Computer Vision. The aim of this event is to develop a Face detection framework from a scratch in matlab . We used the paper of Viola and Jones for reference. It took us 4  workshops to present the overview of the system to a audience who had no previous knowledge about machine learning or image processing!! Personally I think, we may went just too harsh on them. Nevertheless, there were 24 students who sticked till the final day of the event.

The whole procedure looked something like this:

1. workshop 1 ( 4-Jan-2014 ):
• Formation of Integral Images.
• Introduction to convolution and Haar features.
• Basic needs of a face detection framework as a whole.

2.  workshop 2 ( 17-Jan-2014 ):

• What is machine learning?
• Supervised vs Unsupervised learning
• Classification vs Regression.
• Introduction to Perceptron Learning algorithm.
• Challenge 1  released.

3.  workshop 3 (15-Feb-2014 ):

• Code PLA in Matlab step by step.
• Disadvantages of PLA and its ineffectiveness against non-linear separable data.
• Modifications in PLA to make it more flexible.
• A revision of Face detection framework.
• Challenge 2 released.

4. workshop 4 (29-March-2014):

• Framework revision.