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)

Learning Per-Exemplar Distance Functions == Learning Anisotropic Per-Exemplar Kernels for Non-Parametric Density Estimation

When estimating probability densities, one can be parametric or non-parametric. A parametric approach goes like this: a.) assume the density has a certain form, then b.) estimate parameters of that distribution. A Gaussian is a simple example. In the parametric case, having more data means that we will get "better" estimates of the parameters. Unfortunately, the parameters will only be "better" if the modeling distribution is close to "the truth."

Non-parametric approach make very weak assumptions about the underlying distribution -- however the number of parameters is a non-parametric density estimator scales with the number of data points. Generally estimation proceeds as follows: a.) store all the data, b.) use something like a Parzen density estimate where a tiny Gaussian kernel is placed around each data point.

The theoretical strength of the non-parametric approach is that *in theory* we can approximate any underlying distribution. In reality, we would need a crazy amount of data to approximate any underlying distribution -- the pragmatic answer to "why be non-parametric?" is that there is no real learning going on. In a non-parametric approach, we don't a parameter estimation stage. While estimating the parameters of a single gaussian is quite easy, consider a simple gaussian mixture model -- the parameter estimation (generally done via EM) is not so simple.

With the availability of inexpensive memory, data-driven approaches have become popular in computer vision. Does this mean that by using more data we can simply bypass parameter estimation?

An alternative is to combine the best of both worlds. Use lots of data, make very few assumptions about the underlying density, and still allow learning to improve density estimates. Usually a single isotropic kernel is used in non-parametric density estimation -- if we really have no knowledge this might be the only thing possible. But maybe something better than an isotropic kernel can be used, and perhaps we can use learning to find out the shape of the kernel.

The local per-exemplar distance function learning algorithm that I presented in my CVPR 2008 paper, can be thought of as such a anisotropic per-data-point kernel estimator.

I put some MATLAB distance function learning code up on the project web page. Check it out!