Via UCSD Jacobs School of Engineering …
San Diego, CA, March 29, 2007 — A Google image search for “tiger†yields many tiger photos – but also returns images of a tiger pear cactus stuck in a tire, a race car, Tiger Woods, the boxer Dick Tiger, Antarctica, and many others. Why? Today’s large Internet search engines look for images using captions or other text linked to images rather than looking at what is actually in the picture. Electrical engineers from UC San Diego are making progress on a different kind of image search engine – one that analyzes the images themselves. This approach may be folded into next-generation image search engines for the Internet; and in the shorter term, could be used to annotate and search commercial and private image collections.At the core of this Supervised Multiclass Labeling (SML) system is a set of simple yet powerful algorithms developed at UCSD. Once you train the system, you can set it loose on a database of unlabeled images. The system calculates the probability that various objects or “classes†it has been trained to recognize are present – and labels the images accordingly. After labeling, images can be retrieved via keyword searches. Accuracy of the UCSD system has outpaced that of other content-based image labeling and retrieval systems in the literature. The SML system also splits up images based on content – the historically difficult task of image segmentation. For example, the system can separate a landscape photo into mountain, sky and lake regions.
Here are a couple of examples for how how effective the new search engine can be. Asking for ‘blooms’ the system returned the following results:

Asking for ‘mountains’, these results were returned:

Since I am using a lot of images in my blog, I am certainly looking forward to this new technology being applied - it looks like it’ll make blogging much easier
.