Editing Content-based image retrieval (section)

==Technical progress==
The interest in CBIR has grown because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. Textual information about images can be easily searched using existing technology, but this requires humans to manually describe each image in the database. This can be impractical for very large databases or for images that are generated automatically, e.g. those from [[surveillance camera]]s. It is also possible to miss images that use different synonyms in their descriptions. Systems based on categorizing images in semantic classes like "cat" as a subclass of "animal" can avoid the miscategorization problem, but will require more effort by a user to find images that might be "cats", but are only classified as an "animal".  Many standards have been developed to categorize images, but all still face scaling and miscategorization issues.<ref name="Eakins"/>

Initial CBIR systems were developed to search databases based on image color, texture, and shape properties.  After these systems were developed, the need for user-friendly interfaces became apparent.  Therefore, efforts in the CBIR field started to include human-centered design that tried to meet the needs of the user performing the search.  This typically means inclusion of: query methods that may allow descriptive semantics, queries that may involve user feedback, systems that may include machine learning, and systems that may understand user satisfaction levels.<ref name="Survey"/>