Graphviz for Object Recognition Research

Many of the techniques that I employ for object recognition utilize a non-parametric representation of visual concepts. In many such non-parametric models, examples of visual concepts are stored in a database as opposed to "abstracted away" as is commonly done when fitting a parametric appearance model. When designing such non-parametric models, I find it important to visualize the relationships between concepts. The ability to visualize what you're working on creates an intimate link between you and your ideas and can often drive creativity.

One way to visualize a database of exemplar objects, or a "soup of concepts," is as a graph. This generally makes sense when it is meaningful to define an edge between to atoms. While a vector-drawing utility (such as Illustrator) is great for manually putting together graphs for presentations or papers, automated visualization of large graphs is critical for debugging many graph-based algorithms.

A really cool (and secret) figure which I generated using Graphviz somewhat recently can be seen below. I use Matlab to write a simple .dot file and then call something like neato to get the pdf output. Click on the image to see the vectorized pdf automatically produced by Graphviz.

What does this graph show? Its a secret... (details coming soon)

when you outgrow homework-code: a real CRF inference library to the rescue

I have recently been doing some CRF inference for an object recognition task and needed a good ol' Max-Product Loopy Belief Propagation. I revived my old MATLAB-based implementation that grew out of a Probabilistic Graphical Models homework. Even though I had vectorized the code and had tested it for correctness -- would my own code be good enough on problems involving thousands of nodes and arities as high as 200? It was the first time I ran my own code on such large problems and I wasn't surprised when it took several minutes for those messages to stop passing.

I tried using Talya Meltzer's MATLAB package for inference in Undiracted Graphical Models. It is a bunch of MATLAB interfaces to efficient C code. Talya is Yair Weiss's PhD student (so that basically makes her an inference expert).

It was nice to check my old homework-based code and see the same beliefs for a bunch of randomly generated binary planar-grid graphs. However, for medium sized graphs her code was running in ~1 second while my homework code was taking ~30 seconds. That was a sign that I had outgrown my homework-based code. While I was sad to see my own code go, it is a sign of maturity when your research problems mandate a better and more-efficient implementation of such a basic inference algorithm. Her package was easy to use, has plenty of documentation, and I would recommend it to anybody in need of CRF inference.