Yesterday I was in a large meeting with a number of people from across the company. My blogreader was in the corner of my screen, and this post popped up on my screen (screenshot shown on right).

The article is about over-the-air digital video broadcasting (DVB-H), specifically about Quantum's new portable media player that can be used to watch it. The focus of the article is the device, which is interesting.

What caught my eye at that moment was not the device, but the video that happened to be showing on it. The video shows my work buddy and famous Killer Innovations podcaster, Phil McKinney. As a coincidence, Phil was in the meeting and he was actually talking at the moment the article popped on my screen. So, I ripped him a short email that said: "Look!" with the link. This email caught his eye, and after he finished talking, he clicked on the link. And we privately laughed together across the room, because we both thought this was pretty funny.

Later, Phil told me that my email was actually the second email he received about the link. But he clicked on my email first because of the catchy title. So, I surprised him! (I'm just showing off here- We've worked together for a number of years, but I think this is only the 2nd time in my life that I've actually been able to surprise him.) By now, he must have received many more emails about it.

So, my question in all this is Which is better: user-generated tagging or machine-based content analysis?

I was able to spot and get this content over to Phil pretty quickly, and by now I'm sure many other people have as well. Would a machine running a content analysis algorithm linked into a subscription-based alert service have been able to pick this up and get it over to Phil as quickly? Would it have been able to identify Phil? Would it have labelled Phil as part of the content, or just the main content of the story?

There are many researchers working on multimedia analysis. They are trying to create media processing algorithms that can understand what is going on in the pixels of a scene. I do think this is a very important and very hard research problem. There are a number of applications that require you to analyze large existing archives of multimedia streams that do not have any metadata associated with them, so these algorithms can help to process it.

On the other hand, for many applications, users may be around to tag content. These users could tag it much faster and more accurately than a machine. In the web 2.0 and video 2.0 world, make sure to think about the power of the users! Let them help you with the analysis!

Researchers: Please think hard about the broader problem or application that you are trying to solve, and frame the problem accordingly. You don't want to spend too much time working on the wrong problem!

Which is better: Tagging or analysis? Man or machine?

UPDATE
Alex Vorbau poses a better question: What are the best uses of human tagging and machine analysis and how can we make them better?

 
Tags: multimedia search, tagging, content analysis, video 2.0, multimedia, research