Tuesday, April 03, 2007

A really smart image search engine

A smart search with a smart UI ...

"When you search for images on the Web, you use a search engine which relies on the text associated with the pictures -- and not on the images themselves. So the results are sometimes unsatisfactory. But this soon might change because engineers from UC San Diego (UCSD) have developed new algorithms to improve automated image labeling. Their approach could be integrated into the next-generation of image search engines. It's interesting to note that one of the researchers spent six months at Google using a cluster of 3,000 state-of-the-art Linux machines to refine the algorithms, based on what the team calls Supervised Multiclass Labeling (SML). The results obtained by this supervised trained system are pretty good, so it would not be surprising to see Google integrating this method soon.

The figure below illustrates the retrieval results obtained with one word queries for some visual concepts. In this case, the Corel database of images has been queried for "blooms," "mountain," "pool," "smoke," and "woman." As the diversity of the returned images can attest, it seems that this SML system has good generalization ability (Credit: UCSD).

To obtain these results, the SML system was first trained by using a Corel image set, containing 60,000 images with 442 annotations. "The image set was split into 600 image categories consisting of 100 images each, which were then annotated with a general description that reflected the image category as a whole. For performance evaluation, 40 percent of the images were reserved for training (23,878 images), and the remainder (35,817 images) were used for testing."    (Continued via Roland Piquepaille's Technology Trends)    [Usability Resources]

Image Search Engine Results - Usability, User Interface Design

Image Search Engine Results


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