Self Organizing Information

Every day we are overwhelmed by the amount of e-mails, documents, photos and other stuff that we receive on our computer. Most popular software tools such as Outlook and Windows Explorer expect us to handle incoming traffic ourselves and categorize them into maps or folders. As the incoming flow of information is growing, this processing by hand becomes a real burden. It is also often unclear how to categorize items because the item (for example, an e-mail) could be assigned to several categories. This makes it hard to retrieve the correct information when we need it, even using today's search functions. Although categorizing information definitely has its use, we need another approach to make our information really findable and usable.

I suggest that information should organize itself dynamically.



Each information item has content (e.g. text, image, sound) that can be expressed as a set of features (words, color, texture, frequencies etc.) and for each feature we can determine a small-sized signature. We can extract the 'essence' of each information item and use pattern recognition algorithms to calculate the similarity of information items such as e-mails, photos and documents. In my proposed self-organizing environment, e-mails and documents on the same subject will automatically cluster, and so will pictures with similar visual content.



In my Elastic Vision project I am developing algorithms to calculate the similarity of information items and also an algorithm to present information in an organic way to the end-user. In this approach items are not split up and put into buckets (categories), but dynamically shown within a relevant context. Much work is still required to build this approach into a product. For now, you can have a look at the Elastic Vision website for a demonstration of this technique in an image search tool.

Elastic Vision

I have created a new site where you can download my smart image search tool, called 'Elastic Vision'.

Goto Elastic Vision site

It shows each retrieved image in an 'organic' visual network. The images are clustered based on their visual content, enabling you to pick the best from a set of similar images of varying quality.

Here's a small demo of the tool in action. You can find a higher-resolution version on the site itself (just click 'demo').