This interesting addition to the Oracle tool suite is very similar to the interMedia tool. Both have a Java client that lets you retrieve an image file, modify it on your desktop, and then return it to the database. Both Visual Information Retrieval and interMedia use object types with methods as the primary definitions for tables storing the images. Both allow you to update the image record with attributes for size, format, compression, comments, and the like.
The difference between the tools lies in the added query capability of Visual Information Retrieval. This tool can compare images to one another using a scoring method that scores the correlation between two images: 0 (zero) means the images are a perfect match and 100 means the images share no common traits. Visual Information Retrieval also has another method called "similar," which compares two images and rates how similar they are to each other, according to specific criteria. Technology similar to these Visual Information Retrieval methods is used in face-recognition software.

After using the database for a while, I notice a trend that is common to many database applications: the amount of text stored in the database increases. New prospects send in resumes and their resumes are added to the PROSPECT table. Employees are evaluated, and their evaluations are added to the database. As the amount of text increases in the database, so does the complexity of the text queries performed against the database. Instead of just performing string matches, I need new text-search features, such as weighting the terms in a search of multiple terms or ranking the results of a text search. You can use Oracle's interMedia Text (IMT) option to perform text-based searches. In prior versions, this feature was known as the ConText Cartridge. If you have previously used ConText, you may find the configuration for IMT to be simpler and better integrated with the Oracle kernel. You can use IMT to perform wildcard searching, fuzzy matches, relevance ranking ^[1], proximity searching, term weighting, and word expansions.
Search engines almost always provide some form of relevance ranking and present findings to searchers in decreasing order of assumed relevance. The ranking methods normally are based on statistical relations between words in a query and words in a text, anchor words of in-links to the text, the location of the words in the text, and number of in-links to this site from other sites.

[1] Relevance ranking: Relevancy ranking is the method that is used to order the results set in such a way that the records most likely to be of interest to a user will be at the top of the result set. This makes searching easier for users as they will not have to spend as much time looking through records for the information that interests them.