drawing sexy graphs in matlab

Everybody who loves computer sciences loves graphs.  But the fat 'n juicy graphs, the ones with complex structure you just gotta visualize.  To enjoy these beautiful data structures, the hackers at AT&T gave us, the world, Graphviz as a powerful tool for visualizing complex graphs in two dimensions.  I do a lot of stuff in Matlab, so I've put my simple graphviz matlab wrappers up on Github so everybody can enjoy them. I do a lot of stuff with graphs...

My repository, which I'm already using as a submodule in many of my projects, can be found here:
https://github.com/quantombone/graphviz_matlab_magic

Here is a matlab script (included as a Github gist), which should be ran in an empty directory, and it will download a nice mat file plus clone my repo and show the following nice graph.  I perform two graphviz passes where the first one is used to read the graphviz coordinates (from the sfdp embedding) and use Matlab's jet colormap to color the edges based on distances in this space.  In other words, nearby nodes which are connected will be connected by red (hot) edges and faraway nodes will be connected by blue (cold) edges.

The matrix visualized comes from an electromagnetic model, the details can be found here: http://www.cise.ufl.edu/research/sparse/matrices/Bai/qc324.html

The original picture generated by Yifan Hu is here for comparison:

Enjoy
--Tomasz

Scene Prototype Models for Indoor Image Recognition

In today's post I want to briefly discuss a computer vision paper which has caught my attention.

In the paper Recognizing Indoor Scenes, Quattoni and Torralba build a scene recognition system for categorizing indoor images. Instead of performing learning directly in descriptor space (such as the GIST over the entire image), the authors use a "distance-space" representation. An image is described by a vector of distances to a large number of scene prototypes. A scene prototype consists of a root feature (the global GIST) as well as features belonging to a small number of regions associated with the prototype. One example of such a prototype might be an office scene with a monitor region in the center of the image and a keyboard region below it -- however the ROIs (which can be thought of as parts of the scene) are often more abstract and do not neatly correspond to a single object.

The learning problem (which is solved once per category) is then to find the internal parameters of each prototype as well as the per-class prototype distance weights which are used for classification. From a distance function learning point of view, it is rather interesting to see distances to many exemplars being used as opposed to the distance to a single focal exemplar.

Although the authors report results on the image categorization task it is worthwhile to ask if scene prototypes could be used for object localization. While it is easy to be the evil genius and devise an image that is unique enough such that it doesn't conform to any notion of a prototype, I wouldn't be surprised if 80% of the images we encounter on the internet conform to a few hundred scene prototypes. Of course the problem of learning such prototypes from data without prototype-labeling (which requires expert vision knowledge) is still open. Overall, I like the direction and ideas contained in this research paper and I'm looking forward to see how these ideas develop.